promoting knowledge based economy through science technology and innovation essay

Technology and the Innovation Economy

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Darrell m. west darrell m. west senior fellow - center for technology innovation , douglas dillon chair in governmental studies.

October 19, 2011

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Executive Summary

Innovation and entrepreneurship are crucial for long-term economic development. Over the years, America’s well-being has been furthered by science and technology. Fears set off by the Soviet Union’s 1957 launch of its Sputnik satellite initiated a wave of U.S. investment in science, engineering, aerospace, and technology. Both public and private sector investment created jobs, built industries, fueled innovation, and propelled the U.S. to leadership in a number of different fields.

In this paper, I focus on ways technology enables innovation and creates economic prosperity. I review the range of new advances in education, health care, and communications, and make policy recommendations designed to encourage an innovation economy. By adopting policies such as a permanent research and development tax credit, more effective university knowledge commercialization, improving STEM worker training, reasonable immigration reform, and regional economic clusters, we can build an innovation economy and sustain our long-term prosperity.

The Link to Economic Prosperity

Researchers have found a link between technology innovation and national economic prosperity. For example, a study of 120 nations between 1980 and 2006 undertaken by Christine Qiang estimated that each 10 percentage point increase in broadband penetration adds 1.3 percent to a high income country’s gross domestic product and 1.21 percent for low to middle-income nations. [i]

In addition, Taylor Reynolds has analyzed the role of communication infrastructure investment in economic recoveries among OECD countries and found that nearly all view technology development as crucial to their economic stimulus packages. [ii] He demonstrates that there is a strong connection between telecommunication investment and economic growth, especially following recessions. These kinds of investments help countries create jobs and lay the groundwork for long-term economic development.

As a result, many nations around the world are investing in digital infrastructure as a way to jump-start economies weakened by the recent financial collapse. The decline in stock market valuations, rise in unemployment, and reduction in overall economic growth has highlighted the need to target financial resources and develop national priorities. In conditions of economic scarcity, countries no longer have the luxury of being passive and reactive. Instead, they must be proactive and forward-looking, and think clearly about how to create the basis for sustainable economic recoveries.

Not surprisingly, given its long-term potential, a number of countries have identified information technology as a crucial infrastructure need for national development. Broadband is viewed in many places as a way to stimulate economic development, social connections, and civic engagement. National leaders understand that cross-cutting technology speeds innovation in areas such as health care, education, communications, and social networking. When combined with organizational changes, digital technology can generate powerful new efficiencies and economies of scale. [iii]

People Understand Importance of Innovation, But Doubt U.S. Future

Despite the importance of the connection between technology innovation and economic prosperity, public opinion surveys reveal interesting results in people’s views about innovation. A 2009 Newsweek -Intel Global Innovation Survey interviewed 4,800 adults in the United States, China, United Kingdom, and Germany.  Researchers found that “two-thirds of respondents believe innovation will be more important than ever to the U.S. economy over the next 30 years.” [iv]   People understand the basic point that innovation has been key to past prosperity and is vital moving forward.

The survey also found interesting differences between Americans and the Chinese in what they think is important to future advances. According to the survey, “Americans are focused on improving math and science education, while Chinese are more concerned about developing creative problem-solving and business skills.” [v] Apparently, people from the respective nations have different fears about their current innovation training and what is necessary for future innovation.

However, there is a remarkable divergence between Americans and Chinese in assessments of the contemporary situation. Americans are remarkably pessimistic about their own future.  When asked how the U.S. was doing in 2009, only 41 percent of Americans thought our country was ahead of China on innovation compared to 81 percent of Chinese who felt the U.S. was ahead. [vi] Americans worried that their country was falling behind on innovation while other countries were moving forward.

There are objective reasons behind this American pessimism. There are too few Americans studying the traditional STEM fields of science, technology, engineering, and math. Due to our immigration policy, it is difficult for foreign students who are educated in the United States to stay here, get jobs, and contribute to American innovation the way many immigrants have done in the U.S. previously. [vii]   With our current debt and budget deficit levels, Americans worry about our long-term ability to invest in education and research in the way we did in the past and produce positive results.

An analysis of patents granted shows that our country’s long-term dominance has come to an end.  In 1999, American scientists were granted 90,000 patents, compared to 70,000 for those from all other countries. [viii]   By 2009, though, non-U.S. innovators earned more patents (around 96,000) compared to Americans (93,000). This represented the first time in recent years where non-Americans had garnered more patents. [ix]

The United States spends only 2.8 percent of its federal budget on national research and development as a percentage of GDP. This is less than the 4.3 percent spent by the government in Sweden, 3.1 percent by Japan, and 3.0 percent by South Korea, but higher than that of Germany (2.5 percent), France (2.2 percent), Canada (1.9 percent), or England (1.9 percent). Europe as a whole devotes 1.9 percent to research and development, while industrialized nations spend around 2.3 percent. [x]

If one adds together all the science and technology workers in the United States as a percentage of the workplace, 33 percent of American employees have science or technology positions. This is slightly less than the 34 percent figure for the Netherlands and Germany, but higher than the 28 percent in France and Canada,. [xi]

The productivity in this area has fueled considerable demand for those with science and engineering expertise, and it has been difficult for the United States to produce sufficient knowledge workers. [xii]   Thirty-eight percent of Korean students now earn degrees in science and engineering, compared to 33 percent for Germany, 28 percent for France, 27 percent for England, and 26 percent for Japan. The United States has fallen behind in this area.  Despite great demand for this kind of training, only 16 percent of American graduates have backgrounds in science and engineering. [xiii]

In America, the private sector surpassed the federal government in 1980 in terms of the amount of money spent on research and development. By 2003, commercial companies provided 68 percent of the $283 billion spent on research and development, compared to 27 percent from the federal government. Of this total, $113 billion came from the federal government, while $170 came from the private sector. According to information from the National Science Board, the percentage of research and development spending coming from the federal government has dropped from around 63 percent in the early 1960s to 27 percent today, while that of the private sector increased from 30 to 68 percent. [xiv]

The Need for a Clear Focus on Innovation

In moving forward, it is clear that information technology enables innovation in a variety of policy areas.  According to Philip Bond, the president of TechAmerica, “each tech job supports three jobs in other sectors of the economy.” And in information technology, he says, there are five jobs for each IT position. [xv]

Faster broadband and wireless speeds also enable people to take advantage of new digital tools such as GIS mapping, telemedicine, virtual reality, online games, supercomputing, video on demand, and video conferencing.  New developments in health information technology and mobile health, such as emailing X-rays and other medical tests, require high-speed broadband. And distance learning, civic engagement, and smart energy grids require sufficient bandwidth. [xvi]

High-speed broadband allows physicians to share digital images with colleagues in other geographic areas.  Schools are able to extend distance learning to under-served populations. Smart electric grids produce greater efficiency in monitoring energy consumption and contribute to more environment-friendly policies.  Video conferencing facilities save government and businesses large amounts of money on their travel budgets. New digital platforms across a variety of policy domains spur utilization and innovation, and bring additional people, businesses, and services into the digital revolution.

In the education area, better technology infrastructure enables personalized learning and real-time assessment. Imagine schools where students master vital skills and critical thinking in a personalized and collaborative manner, teachers assess pupils in real-time, and social media and digital libraries connect learners to a wide range of informational resources. Teachers take on the role of coaches, students learn at their own pace, technology tracks student progress, and schools are judged based on the outcomes they produce. Rather than be limited to six hours a day for half the year, this kind of education moves toward 24/7 engagement and learning fulltime. [xvii]  

These represent just a few of the examples where innovation is taking place. Technology fosters innovation, creates jobs, and boost long-term economic prosperity. By improving communication and creating opportunities for data-sharing and collaboration, information technology represents an infrastructure issue as important as bridges, highways, dams, and buildings.

Getting Serious about Innovation Policy

To stimulate innovation, we need a number of policy actions. Right now, the United States does not have a coherent or comprehensive innovation strategy. Unlike other nations, who think systematically about these matters, we make policy in a piecemeal fashion and focus on short versus long-term objectives. This limits the efficiency and effectiveness of our national efforts. There are a number of areas that we need to address.

Research and Development Tax Credits : An example of our national short-sightedness is the research and development tax credit.  Members of Congress have extended this many times in recent years, but they generally do this on an annual basis.  Rather than extend this credit over a long period of time, they renew it episodically and never on a predictable schedule.

This makes it difficult for companies to plan investments and pursue consistent strategies over time. Due to political uncertainties and institutional politics, we end up creating inefficiencies linked to the vagaries of federal policymaking. [xviii] While companies in other countries invest and deduct on a more predictable schedule, we shoot ourselves in the foot through a short-sighted perspective.  Bond notes that “23 countries now offer a more generous and stable credit” than the United States. [xix]

Commercializing University Knowledge : Universities represent a crucial linchpin in efforts to build an innovation economy.  They are extraordinary knowledge generators, but must do a better job of transferring technology and commercializing knowledge. University licensing offices must speed up their review process in order to encourage the formation of businesses. Universities should think more seriously about innovation metrics so they allocate resources efficiently and create the proper incentives.

Right now, many places count the number of patents and licensing agreements without much attention to the businesses created, products that are marketed, or revenue that is generated. They should make sure their resources and incentives are aligned with metrics that encourage technology transfer and commercialization. [xx]

STEM Workforce Training and Development : The United States is facing a crisis in STEM training and workforce development. There are many dimensions of this challenge, but one of the most important concerns is the low number of college students graduating with degrees in science, technology, engineering, and math. Few American students are developing proficiency in these subjects, which is hindering the country’s economic future. Past American prosperity has been propelled by advances in the STEM fields.   Skills in these areas helped the country win the space race and the Cold War and we need them now as we transition to a technology driven economy.

To deal with this problem, President Barack Obama’s Council of Advisors on Science and Technology (PCAST) has produced an official report that calls for the creation of a Master Teachers Corps. Among other recommendations, the report emphasizes two actions: 1) hiring 100,000 new STEM teachers and 2) paying higher salaries to the top 5 percent of STEM teachers. [xxi]   However, in an era of budget cutbacks and attacks on teacher unions, it has been difficult to build support for raising teacher salaries in general and adopting differential pay in particular.

In his 2011 State of the Union, the President restated his commitment to putting education at the forefront of the national agenda, emphasizing the need for quality teachers, investment in STEM education programs, and a “bold restructuring” of federal education funding. He called for identifying effective teachers and creating reward systems to retain top-performing individuals.

It is vital to address these issues because basic facts about STEM teaching and competency are not well known.  Failing schools not only harm students, they weaken the overall economy. With the U.S. facing a crisis of massive proportions in terms of its ability to innovate and create jobs, it is imperative that we transform STEM teaching to prepare students for the future economy. Real emphasis should be placed on teacher investment because research has shown that teachers are the primary factor in ensuring student growth and achievement.

An Einstein Strategy for Immigration Reform : We need reasonable immigration reform. One of our most important challenges is a new narrative defining immigration as a brain gain that improves economic competitiveness and national innovation. A focus on brains and competitiveness would help America overcome past deficiencies in immigration policy and enable our country to move forward into the 21 st century. It is a way to become more strategic about promoting our long-term economy and achieving important national objectives. [xxii]

We need to think about immigration policy along the lines of an “Einstein Principle.” In this perspective, national leaders would elevate brains, talent, and special skills to a higher plane in order to attract more individuals with the potential to enhance American innovation and competitiveness. The goal is to boost the national economy, and bring individuals to America with the potential to make significant contributions.  This would increase the odds for prosperity down the road. It has been estimated that “over 50,000 workers with advanced degrees leave the country for better opportunities elsewhere.” [xxiii]

O-1 Genius Visas : In order to boost American innovation, current policy contains a provision for a visa “brains” program. The so-called “genius” visa known as O-1 allows the government to authorize visas for those having “extraordinary abilities in the arts, science, education, business, and sports.” In 2008, around 9,000 genius visas were granted, up from 6,500 in 2004.  The idea behind this program is to focus on talented people and encourage them to come to the United States. It is consistent with what national leaders have done in past eras, where we encouraged those with special talents to migrate to our nation.

However, this program has been small and entry passes have gone to individuals such as professional basketball player Dirk Nowitzki of Germany and various members of the Merce Cunningham and Bill T. Jones/Arnie Zane dance companies. [xxiv] While these people clearly have special talents, it is important to extend this program in new ways and target people who create jobs and further American innovation.  This would help the United States compete more effectively.

EB-5 Job Creation Visas : There is a little-known EB-5 visa program that offers temporary visas to foreigners who invest at least half a million dollars in American locales officially designated as “distressed areas.” If their financial investment leads to the creation of 10 or more jobs, the temporary visa automatically becomes a permanent green card.  Without much media attention, there were 945 immigrants in 2008 who provided over $400 million through this program. [xxv] On a per capita basis, these benefits make the program one of the most successful economic development initiatives in the federal government.

This is a great way to tie U.S. immigration policy to job creation. If a goal of national policy is to encourage investment and job creation, targeted visas of this sort are very effective.  Such programs explicitly link new immigration with concrete economic investment. They also generate needed foreign capital ($500,000) for poor geographic areas. There is public accountability for this policy program because entry visas are granted on a temporary basis and become permanent only AFTER at least 10 jobs have been created.  This kind of visa program is the ultimate in targeting and quality control. Unless the money is invested and leads to new jobs, the newcomer is not allowed to stay in the United States.

H-1B Worker Visas : Right now, only 15 percent of annual visas are set aside for employment purposes.  Of these, some go to seasonal agricultural workers, while a small number of H-1B visas (65,000) are reserved for “specialty occupations” such as scientists, engineers, and technological experts. Individuals who are admitted with this work permit can stay for up to six years, and are able to apply for a green card if their employer is willing to sponsor their application.

The number reserved for scientists and engineers is drastically below the figure allowed between 1999 and 2004. In that interval, the federal government set aside up to 195,000 visas each year for H-1B entry.  The idea was that scientific innovators were so important for long-term economic development that we needed to boost the number set aside for those specialty professions.

Today, most of the current allocation of 65,000 visas run out within a few months of the start of the government’s fiscal year in October.  Even in the recession-plagued period of 2009, visa applications exceeded the supply within the first three months of the fiscal year. American companies were responsible for 49 percent of the H-1B visa requests in 2009, up from 43 percent in 2008. The companies which were awarded the largest number of these visas included firms such as Wipro (1,964), Microsoft (1,318), Intel (723), IBM India (695), Patri Americas (609), Larsen & Toubro Infotech (602), Ernst & Young (481), Infosys technologies (440), UST Global (344), and Deloitte Consulting (328). [xxvi]

High-skill visas need to be expanded back to 195,000 because at its current level, that program represents only six and a half percent of the million work permits granted each year by the United States. That percentage is woefully inadequate in terms of the supply needed. Entry programs such as the H-1B, O-1, and L-1 visa programs grant temporary visas for a period of a few years to workers with special talents needed by American employers. They enable U.S. companies to attract top people to domestic industries, and represent a great way to encourage innovation and entrepreneurship.

Regional Economic Clusters : We need regional economic clusters that take advantage of innovation-rich geographic niches. There are several examples of successful and geographically-based clusters such as Silicon Valley, Boston’s Route 128, and the Research Triangle in North Carolina. In each of these areas, there is a combination of creative talent associated with terrific universities, access to venture capital, and state laws that promote innovation through tax policy and/or infrastructure development.

Research has demonstrated that these innovation clusters generate positive economic results. According to a Brookings report by Mark Muro and Bruce Katz, “it is now broadly affirmed that strong clusters foster innovation through dense knowledge flows and spillovers; strengthen entrepreneurship by boosting new enterprise formation and start-up survival, enhance productivity, income-levels, and employment growth in industries, and positively influence regional economic performance.” [xxvii]

The question is how to promote such clusters in other geographic areas. There clearly are other places with the underlying conditions that foster technology innovation. But Muro and Katz caution that political leaders can’t force clusters that don’t already exist and that they should let the private sector lead in encouraging cluster formation. It is important to leverage existing resources and take advantage of workforce development programs, banking rules, educational institutions, and tax policies. [xxviii]

[i] Christine Zhen-Wei Qiang, “Telecommunications and Economic Growth,” Washington, D.C.:  World Bank, unpublished paper.

[ii] Taylor Reynolds, “The Role of Communication Infrastructure Investment in Economic Recovery,” Working Party on Communication Infrastructures and Services Policy, OECD, March, 2009.

[iii] Erik Brynjolfsson and Adam Saunders, Wired for Innovation, Cambridge, Massachusetts:  MIT Press, 2009.

[iv] Daniel McGinn, “The Decline of Western Innovation:  Why America is Falling Behind and How to Fix It,” The Daily Beast, November 15, 2009.

[v] Daniel McGinn, “The Decline of Western Innovation:  Why America is Falling Behind and How to Fix It,” The Daily Beast, November 15, 2009.

[vi] Daniel McGinn, “The Decline of Western Innovation:  Why America is Falling Behind and How to Fix It,” The Daily Beast, November 15, 2009.

[vii] Darrell West, Brain Gain:  Rethinking U.S. Immigration Policy, Washington, D.C.:  Brookings Institution Press, 2010.

[viii] Darrell M. West, Biotechnology Policy Across National Boundaries, New York:  Palgrave/Macmillan, 2007.

[ix] Michael Arndt, “Ben Franklin, Where Are You?” Business Week, January 4, 2010, p. 29.

[x] Organisation for Economic Co-Operation and Development, Science and Technology Statistical Compendium, 2004.

[xi] Organisation for Economic Co-Operation and Development, Science and Technology Statistical Compendium, 2004.

[xii] Darrell West, Brain Gain:  Rethinking U.S. Immigration Policy, Washington, D.C.:  Brookings Institution Press, 2010.

[xiii] Organisation for Economic Co-Operation and Development, Science and Technology Statistical Compendium, 2004.

[xiv] National Science Board, “Science and Engineering Indictors 2004,” Washington, D.C.:  National Science Foundation, 2004, p. 0-4.

[xv] Philip Bond, “Tech Provides Map for Nation’s Future,” Politico, September 18, 2011.

[xvi] Darrell West, “An International Look at High-Speed Broadband,” Washington, D.C.:  Brookings Institution, February, 2010.

[xvii] Darrell West, “Using Technology to Personalize Learning and Assess Students in Real-Time,” Washington, D.C.:  Brookings Institution, October 6, 2011.

[xviii] Martin Baily, Bruce Katz, and Darrell West, “Building a Long-Term Strategy for Growth through Innovation,” Washington, D.C.:  Brookings Institution, May, 2011.

[xix] Philip Bond, “Tech Provides Map for Nation’s Future,” Politico, September 18, 2011.

[xx] Martin Baily, Bruce Katz, and Darrell West, “Building a Long-Term Strategy for Growth through Innovation,” Washington, D.C.:  Brookings Institution, May, 2011.

[xxi] President’s Council of Advisors on Science and Technology, “Prepare and Inspire:  K-12 Education in Science, Technology, Engineering, and Math for America’s Future,” September, 2010.

[xxii] Richard Herman and Robert Smith, Immigrant, Inc.:  Why Immigrant Entrepreneurs Are Driving the New Economy and How They Will Save the American Worker, Hoboken, New Jersey:  John Wiley & Sons, 2010.

[xxiii] Center for Public Policy Innovation, “Restoring U.S. Competitiveness:  Navigating a Path Forward Through Innovation and Entrepreneurship,” Washington, D.C., September 7, 2011.

[xxiv] Moira Herbst, “Geniuses at the Gate,” Business Week, June 8, 2009, p. 14.

[xxv] Lisa Lerer, “Invest $500,000, Score a U.S. Visa,” CNNMoney.com.

[xxvi] Moira Herbst, “Still Wanted:  Foreign Talent—And Visas,” Business Week, December 21, 2009, p. 76.

[xxvii] Mark Muro and Bruce Katz, “The New ‘Cluster Moment’:  How Regional Innovation Clusters Can Foster the Next Economy,” Washington, D.C.:  Brookings Institution, September 21, 2010.

[xxviii] Mark Muro and Bruce Katz, “The New ‘Cluster Moment’:  How Regional Innovation Clusters Can Foster the Next Economy,” Washington, D.C.:  Brookings Institution, September 21, 2010.

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Toward knowledge-based economy: innovation and transformational leadership in public universities in texas and qatar.

1. Introduction

2. theoretical background, 2.1. transformational leadership traits, 2.2. innovation classification, 2.3. open innovation, 2.4. role of government, industry, and academia in kbe transformation, 2.5. higher education, sustainable development, and engineering, 2.6. engineering education for sustainable development, 2.7. oil-dependant states, 2.7.1. the case of texas, 2.7.2. the case of qatar, 2.8. the united states and qatar in the global innovation index (gii), 2.9. hofstede’s cultural dimension framework, 3. methodology, 3.1. research instruments, 3.2. research procedure, 3.3. sample, 4.1. demographics, 4.2. mlq descriptive statistic, 4.3. system, 4.4. followership, 4.5. correlation between leadership, system, and followership, 4.6. innovative output indicators, 5. discussion.

“The main players in the innovation process are the top administration body. They do the encouragement and pay the money, which is the reason why it is important to have a leader with administrative experience; they do better in management. We need the right culture to support this.” “It is a top-down process. The vision should be from the top, meaning the dean. However, department heads are very powerful in the implementation process and communicate it and stress it to their faculty. “ “It is certainly a top-down process; however, a leader needs a supporting powerful team with highly innovative individuals and early adaptors.”

“An ecosystem needs to be emphasized by the government. For example, they should replace outside consultancy with consultants from the university itself.”

“What the leaders take care of and pay attention to flourish faster, especially when tied to the award system and budget management.” “We need their support in providing more funding for research. It is very stressful because of the high competition.”

“Reducing the gap between faculty and leadership is needed because it minimizes the connection between both, limits the feedback delivered to leadership (from faculty), and limits the direct connection with leadership to describe what is needed exactly from faculty.” “We need better connection. Sometimes changes happen and we don’t know about them in the right time.” “Leaders need to set clear objectives and facilitate large-scale projects, forming teams and putting together mechanisms for funding (which are currently very good), but more is needed.” “Reduced workload in things doesn’t affect innovation or teaching as we have a big amount of reporting.”

“Projects and applied research need better acknowledgement and special reward as they are undervalued.” “I would like leaders to consider having long-term institutional memory for faculty to be eligible for professorship and give more endowment. We have a similar thing, but we need more of it because it helps faculty and encourages them to do more.”

“The government has a good system to define goals, select strong proposals, and follow up. Follow-up is extremely important.” “The proposal review process is very detail-oriented. They check it carefully for achievable tasks and check the faculty’s previous work as part of this review process prior to the selection. It is a transparent fair process here. They care about how to manage the money effectively.” “Government has the responsibility to cultivate a culture for innovation since they drive the funding.”

“We don’t want leadership to be an obstacle. Don’t delay me, but rather help me. A supporting factor here is the fact that it is a flat structure. We have a good autonomy level that helps us be more creative.” “Our leadership gives the right autonomy level to faculty.”

6. Conclusions

Author contributions, acknowledgments, conflicts of interest.

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Al-Mansoori, R.S.; Koç, M. Toward Knowledge-Based Economy: Innovation and Transformational Leadership in Public Universities in Texas and Qatar. Sustainability 2019 , 11 , 6721. https://doi.org/10.3390/su11236721

Al-Mansoori RS, Koç M. Toward Knowledge-Based Economy: Innovation and Transformational Leadership in Public Universities in Texas and Qatar. Sustainability . 2019; 11(23):6721. https://doi.org/10.3390/su11236721

Al-Mansoori, Reem S., and Muammer Koç. 2019. "Toward Knowledge-Based Economy: Innovation and Transformational Leadership in Public Universities in Texas and Qatar" Sustainability 11, no. 23: 6721. https://doi.org/10.3390/su11236721

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Science, Technology and Innovation as Social Goods for Development: Rethinking Research Capacity Building from Sen’s Capabilities Approach

Maru mormina.

Faculty of Humanities and Social Sciences, University of Winchester, Winchester, UK

Science and technology are key to economic and social development, yet the capacity for scientific innovation remains globally unequally distributed. Although a priority for development cooperation, building or developing research capacity is often reduced in practice to promoting knowledge transfers, for example through North–South partnerships. Research capacity building/development tends to focus on developing scientists’ technical competencies through training, without parallel investments to develop and sustain the socioeconomic and political structures that facilitate knowledge creation. This, the paper argues, significantly contributes to the scientific divide between developed and developing countries more than any skills shortage. Using Charles Taylor’s concept of irreducibly social goods, the paper extends Sen’s Capabilities Approach beyond its traditional focus on individual entitlements to present a view of scientific knowledge as a social good and the capability to produce it as a social capability. Expanding this capability requires going beyond current fragmented approaches to research capacity building to holistically strengthen the different social, political and economic structures that make up a nation’s innovation system. This has implications for the interpretation of human rights instruments beyond their current focus on access to knowledge and for focusing science policy and global research partnerships to design approaches to capacity building/development beyond individual training/skills building.

Introduction

The ability to generate scientific and technological knowledge (S&T) and translate it into new products or processes is a key instrument of economic growth and development. Yet, S&T, and societies’ capacity to produce it, is unequally distributed. In light of this inequality, this article offers a normative analysis on the global distribution of S&T capacity. The purpose is two-fold: first, to outline an ethical framework for evaluating different arrangements for the creation and sharing of scientific knowledge globally; and second, to inform policy and funding strategies for developing scientific capacity in low and middle-income countries (LMIC). 1

Lofty aspirations to enhance scientific research and upgrade the technological capabilities of LMIC (UN 2015 ) has led to the adoption of research capacity building (RCB) 2 as a cornerstone of international development assistance (Colglazier 2015 ). Yet, concrete efforts to empower these countries to develop their capacity to produce rather than consume knowledge have been piecemeal. This is unsurprising, given that the concept of capacity remains under-theorised and open to diverse interpretations. Current understandings of capacity are largely Western-centric and rooted on ideas borrowed from disciplines such as performance management, and organisational development (Morgan 2006 ), but also from left-leaning ideals of empowerment, participation and community development (Eade 1997 ). Yet, a clear definition of what constitutes capacity is missing, and consequently, also the frameworks that can help with its assessment, management, monitoring and evaluation (Morgan 2006 ). As a result, practitioners vary considerably in their approaches to capacity building: for some it is as a pure human resources issue, whereas for others is about organisational change and general management. And whilst most international development organisations espouse the basic principle of capacity building as empowerment, in practice it is often operationalised as a means to solve practical problems (ibid).

Lack of conceptual clarity is also seen in donors’ approaches to RCB. Research investments in LMIC aim primarily to the production of research outputs (Enoch 2015 ), often by high income countries (HIC) teams in collaboration with LMIC researchers. RCB is often seen as an ethical requirement to level the playing field between collaborators with unequal capacities and resources for research (Parker and Kingori 2016 ), and thus the focus is strongly on skills development of local scientists. This approach to RCB is popular because it is easier to implement, measure and evaluate, but gives insufficient attention to the wider and long term social factors that help or hinder local knowledge production. Yet, if science is to be harnessed to promote social and economic progress in LMIC, RCB must be viewed as integral to development strategies and approached more holistically at a macro, systems level, not just at a micro, individual level. However, taking such a systems approach begs some normative questions: What is required to build scientific capacity? And whose capacities need to be built?

Efforts to address these questions have been sparse. Some have attempted to link S&T to human rights instruments, specifically Article 27 of the Universal Declaration of Human Rights (UDHR) and Article 15 of the International Covenant on Economic, Social and Cultural Rights (ICESCR), which promote universal access to scientific research and its benefits (Shaver 2010 ; Plomer 2013 ; Chapman and Wyndham 2013 ). These articles have been used to advance two basic ideas: first, that in order to enhance LMIC’s capacity to use S&T for development, these countries must be able to fairly access and benefit from existing knowledge. This creates entitlements to education, access to scientific publications, the promotion of scientific cooperation and international exchanges (Shaheed 2012 ), as well as the lifting of intellectual property (IP) restrictions to utilise knowledge (Shaver 2010 ). Here, the focus is on knowledge transfers from HIC to LMIC with little consideration of the latter’s systemic ability to utilise knowledge, and the relevance of such knowledge to LMIC’s specific needs. Second, that scientific capacity is a good to be distributed to individuals alone. This reduces RCB to strengthening scientists’ technical competencies through education and training without parallel investments to develop and sustain the structures wherein knowledge is created. Thus, whilst the human rights language has the normative teeth to impact upon priority setting and resource allocations decisions by the international community, its usefulness to solve the capacity problem in LMIC is less clear.

This article moves away from discourses of access and knowledge transfers by positing RCB as a tool for knowledge creation. It then makes a normative case for a system approach to RCB built upon a notion of scientific knowledge as a social good , i.e. a good that can only be possessed by and benefit society as a whole. It considers the distribution of this social good (and the capability to produce it) through the lens of the Capability Approach (CA) (Sen 2001 ), expanded to include societies and their institutions. This helps focus not only on the interests of scientists and beneficiaries of the scientific enterprise (patients, consumers, etc.) but on the social structures and processes necessary for the creation and utilisation of knowledge, i.e. on systems. Therefore, an entitlement to a social capability to produce scientific knowledge requires expanding the scope of RCB from a mere empowerment of individuals to the strengthening of the social, political and economic structures that constitute the scaffolding of a nation’s research and innovation system.

The first part of this article critiques traditional understandings of scientific knowledge as an asset, distributed to and possessed by individuals, which in turn determine normative stances and practical approaches to RCB. The second part moves the focus of analysis from knowledge assets to knowledge capabilities , using Sen’s Capability Approach (CA) as a framework. The article argues that scientific knowledge cannot be construed as an individual good in the traditional Senian view (a feature of and valuable to individuals) but as a social good (a feature of and valuable to societies as a whole). If scientific knowledge is a social good, the capacity to produce it is a social capability that emerges from and depends on the social and institutional structures that constitute the condition for knowledge creation. It concludes by briefly examining the implications of this move, outlining how societies’ claim to a capability for producing scientific knowledge requires a re-conceptualisation of RCB as a multilevel approach to strengthen research systems and the societies in which they are embedded.

Scientific Knowledge as an Asset and Research Capacity as Individual - Centred

Knowledge (of which scientific knowledge is a particular type) is mostly discussed in the knowledge management literature as the intellectual capital of organisations, with a focus on the processes involved in its production (Rowley 2007 ). Much of this theoretical work builds upon the Data–Information–Knowledge–Wisdom hierarchy (DIKW), a widely recognised model credited to Russell Ackoff ( 1989 ). DIKW describes a tiered albeit fluid relationship between data, information, knowledge and wisdom: data refers to elements of an observation or recorded descriptions that are disorganised and therefore have no meaning; data is used to create information (data interpreted and organised to convey meaning); information is used to create knowledge and knowledge is used to create wisdom (Rowley 2007 ). Knowledge and wisdom are less well defined, though the former seems to refer to a process of accumulated learning internal to the individual that results from synthesising information from various sources and over time (Keri et al. 2006 ). Wisdom is hardly discussed in the literature (Rowley 2007 ) but implies the application of judgement upon knowledge in order to guide action. Despite lack of consensus on the above definitions and the processes that convert one into the other (Frické 2009 ; Zins 2007 ), the DIKW model is useful to highlight data, information and knowledge as distinct constructs. This distinction is, as explained below, not only semantic but normative. This article is not concerned with a capability to use, produce or share scientific data or information, as this has been done by others (e.g. Bezuidenhout et al. 2017 ) but a capability to generate scientific knowledge, here understood as a process of synthesis and accumulated learning based upon open, systematic and objective empirical observations of the world.

Scientific knowledge underpins much of the technological capacities that fuel the knowledge economy: production and services based on knowledge - intensive activities that contribute to an accelerated pace of technological and scientific advance (Powell and Snellman 2004 ). Because of this increased reliance on intellectual capabilities for wealth production, scientific knowledge is considered an intangible asset necessary for development. Countries must be able to use and exploit knowledge to drive social and economic progress, and this in turn determines approaches to RCB largely aimed at transferring knowledge at the micro level of the individual, a focus the following sections seek to challenge.

Intangible Assets: Consuming Information or Producing Knowledge?

In orthodox economic theories, scientific knowledge is considered a key intangible asset that drives economic development within a country (Romer 1986 ) and reduces technological and economic differences between countries (Abramovitz 1986 ), thus, a private good to be appropriated and commercialised for economic benefit (ibid). Yet, it, can also be said to be a public good, whose use is non-rivalrous (all can use it) and non-excludable (use by one actor does not preclude other actors from benefitting) (Stiglitz 1999 ). This framing has been used to spur action to bridge the knowledge divide between countries by ensuring universal access to essential knowledge and technologies, through initiatives such as IP reforms (Shaver 2010 ), open science/data sharing (Contreras 2010 ), or open access publishing (Chan and Costa 2005 ). A closer scrutiny of these discourses, however, suggests that what is often referred to as knowledge aligns more closely with the definitions of data and information given above. In fact, the knowledge assets contained in patents or scientific publications is a particular type of existing knowledge that can be expressed and shared through formal language, i.e. codified (Polanyi 1966 ) which, because it is external to the agent’s cognitive processes of decoding and interpreting, some argue is simply information (Johnson et al. 2002 ). This type of codified knowledge/information is often assumed to be relevant and applicable to the needs of developing countries and directly transferrable to these contexts (Chan and Costa 2005 ). This is not always the case, as scientific knowledge and derived technologies are purpose-driven and context-dependent (Fu et al. 2011 ). As the bulk of the world’s scientific output is produced by scientists associated with institutions in HIC (Mazloumian et al. 2013 ) and in response to the specific needs of those nations, access to knowledge by LMIC is not straightforward. It requires developing absorptive and adaptive capabilities necessary for its acquisition and subsequent translation into technologies adapted to local conditions.

Thus, construing scientific knowledge as an asset can result in a limited focus on simply transferring and consuming information, a process not always costless. Undoubtedly, facilitating access to scientific information is important and necessary to propel technological development, particularly in the case of innovations entailing leapfrogging, but not sufficient. Much of the knowledge that underpins the innovation process is tacit , not easily embodied, codifiable or readily transferable (Polanyi 1966 ). Tacit (or informal) knowledge is unwritten, unspoken, and based on individuals’ experiences, intuitions, observations, internalised information, and above all interactions. It is this kind of knowledge that results in new or improved products, operational processes or approaches to a social service, i.e. innovation (Frascati-Manual 2015 ). Codification makes existing knowledge/information easily transferrable; yet, in the process, the less tangible aspects of knowledge are lost, and for this reason new tacit knowledge is less mobile. This explains the concentration of knowledge generation capabilities around innovation hubs , and the widening knowledge divide between HIC and LMIC despite globalisation’s erosion of borders and the pervasiveness of information technologies (Gertler 2003 ).

The importance of innovation rarely comes under the scrutiny of global theories of justice, and when it does, the moral discourse appears more concerned with the distribution of the products of S&T innovation rather than the capacity to innovate (Papaioannou 2011 , 2014 ). This capacity depends not only on the ability to absorb codified information but crucially to produce tacit forms of knowledge. This ability remains largely within the domain of HIC (Mazloumian et al. 2013 ), particularly with regards to resource intensive knowledge activities (e.g. genomics). This is not a dismissal of the considerable amount of scientific research and innovation that is currently conducted in LMIC; it is the case that a number of them have embraced S&T as a path to development, though many are left behind. If developing societies are not to be excluded from the benefits of S&T, the geographies of knowledge must be rebalanced through approaches that pay enhanced attention to increasing access and exposure to scientific information whilst also fostering homegrown processes and structures that facilitate the production, translation and utilisation of tacit , situated forms of knowledge.

Research Capacity: A Focus on Individuals

A second assumption is that scientific knowledge, whether as a private or public good, must be possessed and exploited by individuals (scientists) and must benefit individuals (patients, consumers, etc.). Justice, situated at the micro level, is achieved when individuals can equitably access and benefit from scientific knowledge, as proponents of the human rights approach discussed earlier would contend. A strong focus on individuals also underpins the position of Timmermann ( 2014 ). Grounding his argument in the concept of human capabilities, Timmermann suggests that justice demands not only the distribution of knowledge, possessed and consumed as any other good, but equitable participation by individuals in the co-creation of knowledge. This requires building capacity (of individual scientists) where it is lacking (ibid).

Though not all, most approaches to RCB have a strong focus on developing individual skills, perhaps because these are much easier to implement and evaluate (Vallejo and Wehn 2016 ). RCB is usually delivered through HIC–LMIC partnerships (Velho 2004 ), which are generally established not for the purpose of developing capacity but of achieving specific scientific goals—Mode 1 model of knowledge production according to the typology of Gibbons et al. ( 1994 ). As a consequence, RCB becomes embedded and operationalised within specific scientific projects and often reduced to building the technical competencies of individual scientists, most commonly through education and training. This approach to RCB often assumes a straightforward progression from scientific research (knowledge production) to innovation (its application into new technologies) and to development (economic and/or social): input in the form of trained scientists will result in greater knowledge production, more and better technological innovation and faster development. However, not only is this assumption of linearity unrealistic, it also overlooks the systemic nature of knowledge creation and innovation. The pursuit of scientific knowledge is fundamentally a collective process: individuals use their expertise and collaborate in a highly organised division of labour (within and across research teams) to collect data in meaningful arrangements in order to obtain information and produce the tacit forms of knowledge that underpin innovation. This suggests that whilst individuals may have scientific knowledge (in the form of accumulated learning), they alone cannot produce it (Cheon 2014 ), but must participate in a complex web of interactions across many different boundaries: disciplinary, geographic, economic. Moreover, downstream translation of scientific knowledge into technological innovation requires further interplays to enable the transfer of knowledge from centres of “knowledge creation” (typically universities) to “centres of knowledge application” (typically industry) where it becomes added value through its embedding into design (of new products, processes or services). It is important to note however, that this is a non-linear relationship with multiple iterations, feedback loops and failures (Lundvall 2007 ).

Thus, if the process of knowledge creation that underpins innovation rarely takes place outside the specialised formal and informal networks, whether physical or virtual, that constitute a scientific community, then what matters is not so much the strength of individual actors (researchers and research partnerships, universities, firms, markets, governments, etc.) but the connections between them (Velho 2004 ). From this follows that whilst ensuring equitable access to knowledge and a greater supply of trained scientists are both essential to create a critical mass of expertise in a LMIC, this alone does not generate knowledge. An effective approach to RCB must recognise that the creation of scientific knowledge is bound to the social, economic and political institutions, practices and norms that sustain it, to such extent that the right of individuals to access scientific knowledge and participate in its production (Timmermann 2014 ) cannot be asserted without also recognising the intrinsic moral importance of the structures where knowledge is created. The production of scientific knowledge is inherently a social phenomenon situated within the complex enmeshing of social, economic and political relations and for this reason cannot be promoted only at the individual level.

In sum, the argument is twofold: first, what matters from the perspective of justice is not only the fair distribution of existing knowledge and technological innovations, but the fair distribution of the capability to produce scientific knowledge and translate it into new products of innovation. Second, this capability is not a capability of individuals but of societies. The first part is, hopefully, uncontentious. After all, this is the conviction underlying the many RCB initiatives which ultimately aim to grow local scientific capacity. However, they do so instrumentally and mostly focusing on individual capabilities without addressing the social context and institutions that condition individuals’ actions and interactions. The contention here is that only by recognising the inextricable connection between science and the social structures from which it emerges can we develop ways to enhance society’s capacity for creating scientific knowledge. And when we do that, the moral unit of attention is not just the individual but the society.

Two Epistemic Transitions: From Assets to Capabilities and from Individuals to Systems

If viewing scientific knowledge merely as an asset to which individuals alone are entitled leads to narrow and ultimately inefficient RCB strategies, perhaps it is necessary to re-calibrate our understanding of scientific knowledge and its normative dimensions. This section, therefore, moves the focus from knowledge assets to knowledge capabilities and from individuals to the wider social factors that facilitate or hinder knowledge creation processes, i.e. to institutions and systems.

The term capability is perhaps one of the most ubiquitous and ambiguous in the academic literature. It is mainly used in the business literature as organisational capabilities to refer to those intangible assets that enable organisations to manage resources and gain a competitive advantage (Ulrich and Smallwood 2004 ). Perhaps more relevant to the present discussion, the notion of capability is also used (although less extensively) in the economic literature, notably by Abramovitz ( 1986 ), who coined the notion of social absorptive capabilities : people’s technological competences (loosely measured by years of education) and the social, economic and political institutions that influence those competences. For Abramovitz, the rate of technological convergence (the speed at which less technologically developed countries catch up with those more technologically advanced) depends on the social capabilities of a nation. Similar to Abramovitz, Furman et al. ( 2002 ) also apply the term at the country level to explain countries’ differential abilities to innovate and commercialise new technologies ( National Innovation Capability theory). National innovation capabilities do not depend solely on a country’s innovation infrastructure and related outputs, but fundamentally on the environment that determines the innovation process, particularly public policy (e.g. regarding research expenditure, commercialisation, etc.). These different theories point to a notion of capabilities as the ability of firms/countries to do something worthwhile , a process enabled by having access to knowledge assets and influenced by external social or political factors. However, these theories do not explain knowledge creation processes in the first place and above all, because they are mostly descriptive, they do not assist with the normative evaluation of arrangements for the creation of knowledge.

The notion of capabilities proposed by Sen ( 2001 ) allows shifting the informational basis from command over knowledge assets to the ability to produce those assets. Sen’s Capabilities Approach (CA), however, is firmly rooted in the individual, although many have expanded it to acknowledge the existence of social (collective) capabilities (Evans 2002 ; Stewart 2005 ; Deneulin 2008 ; Ibrahim 2006 ; Fernández-Baldor et al. 2012 ). It is from this notion of social capabilities that the CA represents the most appropriate framework to articulate the moral relevance of scientific knowledge and evaluate the social and economic arrangements that impact on societies’ ability to produce it. The remaining sections of this article therefore outline the CA and the rationale for including social capabilities. The article then places scientific knowledge within the domain of social capabilities and sketches some of the implications for RCB.

The Capabilities Approach and its Social Dimension

The CA is an evaluative framework for the assessment of individual wellbeing and social arrangements, and for this reason it is widely used in the design of policies. For the CA, social and economic development is about enlarging what people can be and do, in contrast with other development paradigms that focus on maximising utility or satisfying basic needs by supplying essential commodities (Fukuda-Parr 2003 ). The CA shifts the evaluation of development from the commodities people have or lack to the opportunities open to them. This is obviously relevant for the evaluation of technological progress, which does not depend as much on access to information assets (scientific publications, etc.) as on the capacity to produce locally and socially valuable scientific knowledge, as already argued.

Central to the CA is the notion of capabilities and functionings : capabilities are the real opportunities open to individuals (and, as it will be discussed below, societies) to realise different functionings or achievements that they recognise as important. Capabilities, thus, refer to a particular conception of freedom as the ability to achieve the kind of life one has reason to value. Capabilities are what is effectively possible given individuals’ internal traits and external conditions; functionings are what is actually realised. This distinction is important, as it sets the CA apart from other theories of justice that consider the distribution of utilities (Robbins 1933 ), primary goods (Rawls 1971 ), or resources (Dworkin 1981 ) of intrinsic moral importance. For these approaches only means/resources are inherently valuable; non-material considerations are of no moral relevance. Thus, access to scientific publications, removing IP protections or participating in equitable research partnerships are the ends of distributive justice, but without taking full account of the factors affecting the ability of societies to convert these goods into useful scientific knowledge. By focusing on capabilities as ends, the CA acknowledges the existence and moral relevance of material and non-material constraints to development, i.e. the forces that help or hinder one’s capacity to convert capabilities into functionings, opportunities into achievement. In Sen’s terminology, these are conversion factors .

A key feature of the CA is its strong moral individualism, which emphasises individuals as the sole subjects of moral concern. For Sen, states of affairs must be evaluated only by their effect upon individuals. This is not to say that the CA does not recognise the importance of groups, institutions and other social arrangements ( collectives ) in enhancing or hindering individual freedoms; however, their roles can be sensibly evaluated in the light of their contributions to our freedom (Sen 2001 ), i.e. as conversion factors. Thus, for Sen, collectives enter the evaluative space only insofar they affect individual wellbeing. Many, however, disagree with such instrumentalisation (e.g. Evans 2002 ; Ibrahim 2006 ; Stewart 2005 ; Deneulin 2008 ; Fernández-Baldor et al. 2012 ), arguing that collectives are not just a means for realising individual freedoms; they are constitutive to those freedoms.

Asserting the constitutive importance of collectives rests upon a fundamentally relational conception of individual freedom: a social phenomenon defined against its specific historic, social or political context (Otano-Jiménez 2015 ). The individual focus of the CA offers a robust defence of individual freedom but cannot help to identify the processes necessary to promote those freedoms. A relational conception of freedom, instead, provides an analytical lens to understand social commitment to individual freedom. Here, the starting point of analysis is not so much the individual but the forms of solidarity that enable the expansion of individual capabilities through the establishment of just social institutions (ibid) . Such a broadened focus requires ancillary concepts such as social capabilities : capabilities that can only be achieved by individuals through their participation in social institutions ( collectives ). Social capabilities emerge from the exercise of collective agency in ways that are more than the sum of individual capabilities (Stewart 2005 ), and their benefits cannot be achieved by individuals alone (Ibrahim 2006 ).

The idea of social capabilities remains contested (Robeyns 2005 ; Alkire 2008 ; Cleaver 1999 ). A key concern is that any attempt to move the focus away from the individual may overlook the dynamics of inequality and exploitation within groups/societies that may negatively impact upon individual freedoms (Cleaver 1999 ). Social capabilities enable the achievement of goals that cannot be realised by individuals alone but can also lead to exclusion (e.g. ethnic discrimination) or bring about negative consequences for individuals (e.g. oppression of women or minorities within groups). Thus, while some see collectives as enabling and intrinsic to human flourishing (Ibrahim 2013 ) others see them as potentially repressive and thus instrumentally valuable only insofar they do not oppress individual agency. In other words, both Sen and his critics recognise the importance of collectives and their relationship with individual freedom, but they disagree (1) on the nature of this relationship—instrumental or intrinsic-, and (2) their potential to oppress or enhance those freedoms. Before proceeding to consider S&T as a collective capability, let us briefly address these two disagreements.

The rationale for considering collectives intrinsically valuable beyond their contribution to the lives of individuals can be found in the concept of irreducibly social goods (Taylor 1995 ): goods that cannot be reduced to individual acts or choices since those acts and choices are only possible through collective agency. Language and culture are paradigmatic examples of irreducibly social goods, as they cannot be reduced to individual utterances but only exist within a set of shared norms and codes shaped by collective agency. For Gore ( 1997 ), institutional arrangements are also irreducibly social goods, since they are the codes and practices that constrain and enable human activity, and at the same time they are themselves constituted through that activity (ibid). Irreducibly social goods, such as language, culture, institutional arrangements and, as it will be argued shortly, knowledge, cannot come into being through individual agency (they are not the goods of individuals but of society). They have value beyond the individual because they do not benefit individuals but society as a whole (they are not goods for individuals but for society). Failing to recognise their intrinsic value by incorporating them in the evaluation of development only as instrumental to individual wellbeing is failing to recognise the intimate connection between the individual and society. Acknowledging social goods as intrinsic to individual wellbeing adds an important layer to the evaluation of states of affairs. This point is important: it does not mean that individual wellbeing should be subsumed within collectives but that both should enter the evaluative domain.

If irreducibly social goods are constituted through the activities of individuals in ways that are more than the sum of the parts, the social capability to produce such goods is also constituted through the capabilities of individuals in ways that are more than just the sum of individual capabilities. For example, the capability for democratic processes depends upon individuals having the freedom to vote and express their views without fear, yet cannot be reduced to these individual freedoms: it requires concerted action. Social capabilities, thus, emerge from the interconnected actions of individuals (and their capabilities) within societies. Collective capabilities do not exist without individual capabilities. At the same time, individual freedoms can only be understood against the collective capability that enables them.

There remains of course the issue of inequality and oppression. Proponents of social capabilities have yet to provide a satisfactory solution to the tension between the individual and the collective, for a focus on the latter can obscure internal dynamics of inequity that oppress individual freedoms (for example, when empowering groups suppresses minority voices or leads to inequities in the way interests are aggregated). However, the same holds for the individualistic view too, for example, when enhancing one capability leads to inequality with regards to other capabilities. One may endorse compulsory primary school education because being able to read and write is a basic capability. Yet, in poor societies, this may disproportionately affect households that critically depend on child labour for their subsistence (another basic capability). Thus, when capabilities conflict, a focus on the individual does not necessarily lead to enhancement of individual freedoms. On the other hand, the inextricable link between social and individual capabilities means that the more the latter are enhanced, the more the former are empowered, and vice-versa. In other words, a well-functioning society is only possible when all individuals are empowered through equality of opportunity. At the same time, individual empowerment necessitates the existence of strong social institutions. Acknowledging the importance of social capabilities need not be acritical but requires an evaluative framework to determine which ones strengthen the process of development and expansion of freedoms and which do not, just as evaluative frameworks are needed to distinguish between good and bad individual capabilities.

Scientific Knowledge as an Irreducibly Social Good

Despite recognising the importance of S&T for development and its positive and negative impact on political and economic relations within and between countries, much of the S&T literature lacks a normative direction. Normative discourses, on the other hand, have engaged with S&T mostly from the perspective of its potential harms and benefits to individuals. Distributive justice concerns have mostly been raised in the context of access to existing scientific knowledge—see, for example, the work of Pogge ( 2011 ) on access to essential medicines—rather than on the capacity to generate such knowledge. This article shifts the focus of analysis from access to knowledge to capabilities, here considered not at the micro level of individual empowerment but at the macro level of systems and institutions strengthening. Such a move is achieved by construing scientific knowledge as an irreducibly social good , and the capability to produce it as a social capability that depends upon the existence of adequate social institutions. It is important to consider research capacity holistically and as a currency of justice if S&T policies are to have a substantial and lasting impact on development.

Sen’s CA helps to articulate the claim that equitably sharing in the benefits of S&T requires not so much the distribution of data, information or existing codified knowledge but the distribution of the capability to produce and use new scientific knowledge (tacit at first and subsequently codified) as a pre-requisite for human and economic development. Such a capability is a social capability because scientific knowledge is an irreducibly social good: it cannot be reduced to individual acts of learning but is situated within a scientific culture (codes, institutions and practices) and co-evolves with it. That is, scientific knowledge determines and is determined by its specific social context (e.g. when social values determine what scientific questions count as important, and the answers to those questions in turn shape social values). In this sense, scientific knowledge is an irreducible feature of society and not of individuals. Scientific knowledge (especially basic or non-applied knowledge) is not instrumental to individual wellbeing and cannot be judged through its effects on individuals since it cannot be directly applied to them (e.g. understanding the relationship between folic acid, mood and cognitive function is of no direct benefit to individuals but can help the scientific community to develop effective treatments for depression or dementia). In this sense, scientific knowledge is a social good, valuable to society as a whole insofar it expands its opportunities for developing the processes and applications (vaccines, medicines, etc.) necessary for advancing individual wellbeing.

Thus, if scientific knowledge is an irreducibly social good (more than the sum of individual research efforts and benefits society rather than individuals), the capability for knowledge creation is best conceived as a social capability. It creates a critical mass of expertise that is essential for innovation and is valuable to society for its self-realisation. This has a completely different set of implications from an evaluation of knowledge production simply in terms of its contribution to individual capabilities. In the classical Senian approach, the value of scientific knowledge would be relevant only insofar it improves the lives of individuals, i.e. as an ingredient of individual human wellbeing. The upshot is that only knowledge that is directly applicable would count as valuable, which automatically disqualifies most of the scientific enterprise. As a social capability, however, the ability to produce scientific knowledge is valuable beyond its actual benefits to single individuals; it expands society’s innovation capital, thus diversifying and widening the range of possible solutions to its specific problems. In other words, scientific knowledge is part of a nation’s intellectual capital (competencies, knowledge, skills) that sustains development. Seen from the perspective of the CA, therefore, scientific knowledge creation becomes part of the capability set that can empower developing societies to redraw the boundaries of development and as such it cannot be construed as an individual good.

There are of course, two important objections to the above argument. First, construing scientific knowledge as a social good valuable to society as a whole can mask potential uses in ways that hamper individual wellbeing (e.g. when scientific knowledge is used in warfare), or that advance the wellbeing of certain individuals/groups over others. For example, in highly stratified societies, the production of scientific knowledge may be disproportionately directed towards addressing the health needs of higher socioeconomic groups, thereby neglecting minorities. Yet, while this criticism is a potential limitation of the present argument, it is important to draw a distinction between the capability to produce scientific knowledge (which needs to be evaluated at the level of society) and the application of such knowledge to develop technologies, medicines, etc., that advance (or not) individual wellbeing. As pointed out above, collective capabilities exist alongside individual capabilities as two sides of the same coin. Sen acknowledges the existence of valuable and non-valuable capabilities (Stewart 2005 ); in the same vein the existence of good and bad social capabilities can be posited according to how these affect individuals within their societies. Thus, whilst the capability to produce scientific knowledge requires collective empowerment (in the form of policies, institutions, etc.), how such capability is used must be morally evaluated in terms of equitable individual empowerment if the abovementioned issues of discrimination, corruption and nepotism are to be avoided.

Second, construing scientific knowledge as a feature of society, i.e. as an endeavour that requires collective agency, does shine a light on the need to strengthen the institutions and structures for knowledge creation through a holistic approach to RCB. However, it can also downplay the critical role that individual agency has in the process of knowledge creation and thus the importance of creating the right set of conditions for individuals to flourish through meaningful and fair participation in the collective production of knowledge (Timmermann 2017 ). In other words, focusing on institutional strengthening can lead to treating individual scientists mainly as contributors to the process of knowledge production overlooking the fact that they also benefit from it. Though these are important concerns, they stem from positing a false dichotomy between individuals and society. If, as argued before, social capabilities depend upon the existence of individual capabilities and vice-versa, strengthening social institutions requires paying attention to how individuals are empowered and benefit from the production of knowledge. Construing knowledge as a social good calls for a holistic approach to RCB aimed at creating enabling environments for S&T through adequate institutional arrangements. These must include opportunities for individual scientists’ development (e.g. training), as well as incentives that reward collegiality (for example through data sharing), rigour and academic excellence (Rappert and Bezuidenhout 2016 ). The relationship between individual and social capabilities should not be seen as exclusory but reciprocal. Individuals’ scientific capabilities depend upon social arrangements (e.g. public policies on education, employment, and commercialisation of scientific findings, participation in scientific networks, etc.). They are socially dependent individual capabilities (Davis 2015 ). At the same time, a social capability to produce knowledge crucially rests upon individuals’ commitment to the scientific community and the scientific endeavour. That is, they are individually dependent social capabilities (ibid) . For this reason, the dichotomy between individual and social capabilities is fallacious (at least at the theoretical level), for both are interdependent.

On the operational level, however, individual and collective capabilities can clash, for a focus on the collective can obscure inequities in the allocation of resources among members of a scientific community, as well as discriminatory practices resulting in the reinforcement of scientific elites, inequitable access to opportunities for education and training or even exploitation of under-recognised categories of scientists/workers (Timmermann 2017 ). For a collective capability to produce knowledge can be achieved through institutional arrangements that advantage some and disadvantage others, and a focus on collective social institutions, as discussed above, needs to contend with the problem posed by the inevitable aggregation of interests. This is not just a problem for the CA but for public policy in general. However, if knowledge creation is conceived as a capability that empowers societies to pursuit their own self-defined goals, as stated above, the ways in which knowledge is created matter instrumentally. For by fostering diverse and inclusive scientific communities, societies ensure the breeding of a wide range of scientific ideas, which in turn expand the range of possibilities for innovation and consequently development. Thus, the evaluation of social arrangements can be made on the basis of how well or badly they promote the production of the broad base of scientific ideas necessary for innovation and development.

The Social Capability for Scientific Knowledge: Implications for RCB

Recognising that scientific knowledge is an irreducibly social good whose realisation depends on the existence of social capabilities requires moving the focus of analysis towards what Deneulin ( 2008 ) defines as structures of living together : a concept originally coined by French philosopher Paul Ricoeur to describe the social institutions within a historical community (people, nation, region): a structure irreducible to interpersonal relations and yet bound up with these (Ricoeur 1992 ). Social institutions, according to these authors, are characterized by ‘a bond of common mores’ (ibid) from which power in common —the capacity to act together—emerges. In other words, social institutions are constituted by individuals bound by common norms, codes and practices in ways that transcend interpersonal relations (Ricoeur refers to the enmeshing of relationships that encompass the plurality of distant others). It is because of this indivisibility that empowerment through collective action is more than the sum of individual efforts. Social structures, therefore, are the locale where empowerment occurs and social goods can be realised, and for this reason they matter beyond their effect upon individuals. From this follows that the production of scientific knowledge by individuals interconnected through common norms and practices is intrinsically bound to the local social structures where those relationships (and the knowledge that emerges from them) are formed. Consequently, an entitlement to the capability to produce scientific knowledge entails a corresponding obligation to assistance to strengthening the necessary processes and institutions, i.e. the connections between actors within the innovation system. This broadens the scope of justice beyond the development of individual capabilities and requires an approach to RCB beyond the individual level.

This article posited scientific knowledge as an irreducibly social good : a good that does not belong to individuals (in the sense that it cannot be reduced to individual acts of learning) and has value beyond its contribution to individual wellbeing (in the sense that it does not benefit individuals directly but society as a whole by expanding its opportunities for innovation). Paraphrasing Ricoeur and Deneulin, scientific knowledge emerges from the structures of knowing together , that is, from the array of social institutions and the interactions between them. The importance of institutions in the creation of scientific knowledge and innovation is not new but can be traced to the influential concept of National Innovation Systems (NIS) (Freeman 1989 ; Lundvall 1992 ), which emphasise the role of institutions in creating and sustaining environments that enable the production and sharing of collective knowledge and resources for the pursuit of social, technological and economic innovation. However, whilst the NIS concept (and its various subsequent derivations) has been useful in highlighting the systemic nature of knowledge production, it is almost exclusively concerned with the commercialisation of innovation, and therefore decisively centred in the firm (Godin 2009 ), with others institutions (government, universities, industry, non-profit, etc.) providing only a supporting role (Watkins et al. 2015 ) and being defined by and devoted to this commercialisation end (Godin 2009 ). Moreover, and paradoxically, the NIS approach does not connect the processes of knowledge creation and diffusion with the political processes of institutional capacity strengthening and governance. The normative approach proposed here bridges this divide; considering knowledge creation processes as a social good on one hand reaffirms the systemic (social) nature of scientific knowledge (a good of society), and on the other makes explicit the relationship between scientific knowledge and its ultimate goal: social transformation (a good for society). This dual social dimension of scientific knowledge, in turn, brings to the fore the moral importance of social and political institutions (governments, universities and research institutions, scientific societies, funders, patient organisations and other civil society groups, industry, etc.) not just as mere facilitators of knowledge creation but as mutually interdependent enablers and constrainers of behaviours, agendas and ultimately performance. This provides a strong rationale for new approaches to RCB that focus on the strengthening of institutional and governance processes alongside traditional technical skills building.

In practice, this means shifting the focus of RCB initiatives from the individual researcher to the social environment that facilitates or hinders knowledge creation processes. If scientific knowledge is a social good that emerges from the social structures of living and knowing together, a broader approach to RCB is needed, one that moves beyond an almost exclusive focus on individuals and to some extent research infrastructure (Beran et al. 2017 ) towards creating enabling institutional, organisational and policy environments for the conduct and translation of research and its embedding into public policy. Strengthening capacity at the level of the individual is not optional; on the contrary, workforce development is the backbone of research systems. However, long-term and sustainable research and innovations systems require a multilevel approach that addresses the multiplicity of disabling factors common to most developing countries: technical know-how and resources for sure, but also insufficient ownership of research agendas (still largely dominated by Western donors), geographic isolation and peripheral engagement with the global scientific community, inadequate engagement with users of research (industry, communities and notably policy makers), and above all lack of political buy-in and supportive public discourses (for example, climate change in the case of renewable energies). It has been argued that in most developing countries it is not the lack of a trained workforce or specific technical competencies but the insufficient coordination between the different components of the innovation system that hinders the process of knowledge creation (Arocena and Sutz 2000 ). Highlighting the normative importance of the structures of knowing together , therefore, sheds light on this often-missing relational dimension of knowledge creation.

Is RCB Neocolonialist ?

In arguing for an entitlement to a process of development that includes scientific knowledge creation, some may see the threat of neocolonialism. Does RCB endorse, if not impose, a Western paradigm of development grounded in some form of technological determinism, i.e. the assumption that scientific and technological development drives social and human development (Cherlet 2014 )? Fully addressing this concern is beyond the more modest aims of this article but below are a few pointers to frame further discussion.

This paper uses an understanding of scientific knowledge (the philosophical worldview, activities, and social institutions that since the Scientific Revolution are identified as modern science) which has historically contributed to a Eurocentric account of progress. Technological progress helped the West portray itself as developed, civilised and rational, in contrast with a rest of the world that was undeveloped, savage and irrational (Harding 1994 ), thus justifying centuries of colonial domination (Seth 2009 ). From this perspective, any attempt to build or strengthen scientific research capacity in LMIC may be seen indeed as a neocolonialist imposition. However, without denying that some approaches to RCB may be questionable for their disregard of local agency and values, neocolonialist labels are unhelpful as they preclude more nuanced analyses of broader power and social justice issues (Horton 2013 ). For example, HIC’s framing of aid (including RCB) in the national interest subordinates development priorities to the needs of HIC. This compromises the ability to bring clear benefits to LMIC because it denies them agency and fails to create the open forms of governance and alliances necessary to respond to development challenges. Dismissing RCB as neocolonialist does little to address these issues and to strike the right balance between benefits accrued to HIC and long-term benefits given to LMIC.

RCB, understood as a process of capability expansion, helps create a new balance of power by redrawing the geographies of science. The history of Western science is a history of culturally biased patterns of systematic knowledge and systematic ignorance (Harding 1994 ), for the questions that came to count as scientific were those whose answers benefitted colonial powers: improvement of land and sea travel, identification of economically valuable indigenous species, or understanding tropical diseases to maintain the colonies healthy and economically viable (Lock and Nguyen 2010 ). Other aspects of nature which did not benefit the expansionist West remained uncharted. Because of the still uneven geographic concentration of scientific capacity, many of these biased patterns of knowledge creation have remained even after decolonisation and global scientific priorities (and funding) continue to be established with the tunnel vision of developed countries’ needs. Empowering LMIC to strengthen scientific knowledge production processes can thus help redress the epistemic biases abovementioned by expanding the range of global research actors and, consequently, definitions, priorities and agendas beyond reductionist understandings of progress based on Western-construed categories.

Neither does RCB impose a Western paradigm at the expense of non-Western approaches and forms of knowledge. This implies an artificial epistemological distinction between Western and non-Western knowledge given (a) the diversity within each construct and (b) the fact that what is defined today as traditional non-Western knowledge has been in contact and extensively influenced by Western knowledge for centuries, and vice-versa (Agrawal 1995 ). Moreover, this article argues for strengthening indigenous S&T capacity, and this presupposes the enmeshing of local knowledge in the scientific enterprise, and a significant degree of control over knowledge-creation processes that precludes any attempt to impose exogenous cultural constructs. Nonetheless, it is important to recognise that indigenisation of scientific knowledge cannot be achieved without a deep engagement with the values and aspirations of the communities such knowledge is intended to benefit (Fejerskov 2017 ). Although this is implicit in the present argument, a more detailed analysis is required in order to devise effective approaches to community engagement, especially in countries deeply stratified along socioeconomic and ethnic lines.

Finally, RCB does not downplay the considerable research that takes place in LMIC (particularly emerging knowledge economies) but emphasises the need to further shift the geographic boundaries of science. The great scientific contributions of the non-Western world are largely forgotten and need re-appropriation. RCB does not ignore the economic and political agency of LMIC, but acknowledges that the pressures of global market forces and the disruptive effects brought forth by rapid technological change can expand or restrict economic, political and social opportunities (Archibugi and Pietrobelli 2003 ). Most LMIC recognise with a sense of urgency that these opportunities cannot be fully exploited by simply consuming S&T (Fu et al. 2011 ; Ghani 2017 ). The approach to RCB advocated in this article, thus, responds to this recognition.

Conclusions

Scientific knowledge remains unequally distributed, but more so is the capacity of societies to produce it. Although strengthening the scientific capacity of developing countries is a priority for development cooperation, these efforts are not underpinned by a properly articulated theory of justice. Rather, they seem to rest upon two implicit assumptions: first, that closing the capacity gap requires fairer access to codified knowledge/information; second, that scientific knowledge is a good to be distributed to individuals alone, thus reducing RCB to strengthening scientists’ technical competencies through education and training without parallel investments to develop and sustain the social structures that facilitate knowledge creation.

This article problematises these assumptions by showing the limitations of a focus on the distribution of existing knowledge, not always relevant to the needs of LMIC and not always utilisable due to lack of adequate structures for the translation of such knowledge into social and economic development. The CA is therefore used here as a justice framework to move beyond issues of access and command over knowledge assets and articulate the idea that what matters for development is the distribution of the capability to produce knowledge, thus highlighting a moral case for assistance to RCB. Though RCB has been a development priority since the 1990s, a clear understanding of what exactly constitutes research capacity is missing. Consequently, RCB interventions have focused on enhancing individuals’ competencies through education and training, also because these are relatively easier to implement and evaluate. Such approaches, however, have not taken sufficient account of the need to strengthen the social, political and economic structures that connect the different components of a nation’s innovation system. This represents a moral blind spot that masks important questions regarding the economic and political arrangements that help or hinder the scientific divide between rich and poor countries.

Using the concept of irreducibly social goods and expanding Sen’s CA approach to include collectives, scientific knowledge is framed as a social good. Consequently, the (social) capability to produce such good requires the strengthening of the social structures for the production of knowledge. This has implications for the interpretation of the human right to science and culture (Article 27 of the UDHR) beyond its current focus on access to scientific knowledge, and for focusing science policy and global research consortia to design holistic approaches to capacity building beyond individual training/skills building.

Scientific capabilities are shaped by country-specific political and institutional contexts, and are thought to reflect countries’ different trajectories of development and patterns of strengths (Bartholomew 1997 ). Seen from this perspective, scientific development is a local phenomenon rooted in the knowledge, skills, etc. accumulated over time and which constitute a nation’s innovation capital, its preferred solution for advancing development. Scientific knowledge as a social good and knowledge creation as a social capability emphasise the importance of construing S&T as spatially and temporally situated, and therefore of paying attention to the unique enmeshing of historic, cultural and social influences that determine the institutional landscape of local research and innovation systems and their functioning. This should warn funding bodies and capacity building experts against the temptation of simply transferring decontextualized blueprints or re-packaging solutions mechanistically—a one-size-fits-all approach. Instead, it calls for more flexible and innovative ways of fostering capacity, beyond simply developing skills so that scientists may fit some pre-defined model, but supporting people, organisations and institutions to challenge current states of affairs and effect change. The idea of social capabilities grounds S&T in its specific social milieu and calls for research leaders and policy makers in LMIC to view capacity development as above all an endogenous and participatory process that requires paying attention to and engaging with society’s specific needs and attitudes (e.g. with regards to emerging technologies). It also challenges them to focus on the bigger picture and shape political agendas from the bottom up. Lastly, it calls for local leaders to challenge the seriously flawed model of capacity building that assumes that external actors know better what their capacity needs are.

The growth of transnational research networks is dissolving national borders, suggesting that S&T is also a global process (Bartholomew 1997 ) consisting of converging standards and complex governance processes. This may throw into question the relevance, or even possibility, of local research and innovation systems grounded in contextual specificity as argued above. This tension between the local and the global may be resolved by acknowledging the need for differentiated scientific capabilities that on one hand respond to local knowledge needs, and on the other enable synergistic relationships for the tackling of common problems. In this regard, local innovation remains relevant not just because it better serves local demands, but also because it diversifies and widens the range of possible solutions to global technological problems. This provides a powerful incentive for international cooperation and justifies global action for assisting LMIC to strengthen their local research systems.

Acknowledgements

I would like to thank Dr. Sridhar Venkatampuram for helpful discussions on earlier versions of this manuscript and the two anonymous reviewers for their constructive comments which helped me improve the clarity and quality of the argument. A special thanks to the eagle-eyed editor who proofread the final version and provided further interesting insights.

1 This is the traditional World Bank classification based on Gross National Income (GNI). It has been suggested to be too broad to be distinctive given it groups together countries with different indicators of development, including scientific capacity. Throughout this article, LMIC refers to countries in the low and lower-middle income distributions and excluding upper middle countries (e.g. Argentina, South Africa and Mexico), which are included in the upper-middle distribution as high-income countries (HIC). Whilst recognising that scientific capacity does not necessarily correlate with GNI, the LMIC–HIC classification was chosen for simplicity, and on the basis that overall most countries along the low-middle income distribution tend to underinvest in science and technology, compared to countries in the upper and high-income distributions (Rabesandratana 2015 ), although this is not an absolute rule.

2 Whilst some commentators prefer to use the term ‘capacity development’ instead of ‘capacity building’ to emphasize that abilities are strengthened and enhanced, rather than built from scratch (Vallejo and When 2016 ), in practice there is little operational difference between the two terms. Therefore, ‘capacity building’ is used here as equivalent to ‘capacity development’.

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Open Access

Peer-reviewed

Research Article

Global UN 2030 agenda: How can Science, Technology and Innovation accelerate the achievement of Sustainable Development Goals for All?

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Africa Sustainability Innovation Academy (ASI-Academy), Lagos, Nigeria, Department of Technology, Management and Economics, Technical University of Denmark, Denmark, The Institute for Sustainable Development, First Technical University, Ibadan, Nigeria

Roles Formal analysis, Writing – review & editing

Affiliation Division Agri-Food Marketing and Chain Management, Department of Agricultural Economics, Ghent University, Belgium

Roles Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

Affiliations The International Fund for Agricultural Development (IFAD), Nairobi, Kenya, The International Center for Tropical Agriculture (CIAT)

Roles Formal analysis, Writing – original draft, Writing – review & editing

Affiliation School of Agriculture and Environment, University of Western Australia, Australia

Roles Formal analysis, Methodology

Roles Data curation, Writing – original draft

Affiliation Life Cycle Sustainability, Eco Intelligent Growth, 08174 Barcelona, Spain

Roles Formal analysis, Writing – original draft

Affiliation World Bank, Washington DC, United States of America

Roles Data curation, Formal analysis, Writing – original draft

Affiliation The Institute of Technology on Immunobiologicals (Bio-Manguinhos), Rio de Janeiro, Brazil

Affiliation Global Health Programme, Chatham House, London, United Kingdom

Affiliation School of Environment, Yale University, United States of America

Affiliation School of Computer Science, University of Sydney, Australia

Roles Data curation

Affiliation Global Change Institute, University of the Witwatersrand, South Africa

• Ademola A. Adenle, 
• Hans De Steur, 
• Caroline Mwongera, 
• Fay Rola-Rubzen, 
• Marcia Dutra de Barcellos, 
• David F. Vivanco, 
• Govinda R. Timilsina, 
• Cristina Possas, 
• Robyn Alders, 

• Published: October 30, 2023
• https://doi.org/10.1371/journal.pstr.0000085
• Peer Review
• Reader Comments

9 Feb 2024: Adenle AA, De Steur H, Mwongera C, Rola-Rubzen F, de Barcellos MD, et al. (2024) Correction: Global UN 2030 agenda: How can Science, Technology and Innovation accelerate the achievement of Sustainable Development Goals for All?. PLOS Sustainability and Transformation 3(2): e0000100. https://doi.org/10.1371/journal.pstr.0000100 View correction

The adoption of 17 sustainable development goals (SDGs) with 167 targets by the United Nations member states in 2015 emphasizes the critical role of science, technology and innovation (STI) in addressing sustainability challenges, including poverty, hunger, health, employment, climate change and energy. However, STI plays a limited role in the context of the global agenda of 2030 and for achieving SDGs in low- and middle-income countries. The perspectives of relevant stakeholder groups (i.e., policymakers, academia, donors, private sector, and non-governmental organizations) were assessed through an international survey on the role of STI in tackling SDG challenges in three main themes: agriculture, health, energy, and environment. Our findings reveal that human resource capacity on STI is still fragile in many developing countries, including some middle-income economies, suggesting that to achieve Sustainable Development Goals (SDGs) 1, 2, 3, 7, and 13, it is necessary to strengthen the educational system, increase investment in research and development programs, implement staff retention policies, foster collaboration, and provide adequate infrastructure and expertise for the required skills and competencies to promote cooperation in science, technology, and innovation (STI).

Author summary

STI will play a critical role in achieving the sustainable development agenda such as the SDGs. Achieving the SDGs requires a strong national innovation system that encourages the implementation of an STI framework at the heart of government policy, and this entails building a comprehensive and robust STI system based on understanding the interaction between actors and the dynamics of STI governance. However, numerous countries worldwide are struggling to devise new STI policies that can effectively tackle the unique challenges posed by SDGs. Based on the perspectives of various stakeholders, we highlight the issues surrounding STI’s role in tackling some SDG challenges. We present a framework for STI cooperation for the SDGs focusing on four dimensions: national planning, resource and capacity building, engagement and partnerships, and access to innovation to deal with the challenges and issues in incorporating STIs into achieving the SDGs.

Citation: Adenle AA, De Steur H, Mwongera C, Rola-Rubzen F, de Barcellos MD, Vivanco DF, et al. (2023) Global UN 2030 agenda: How can Science, Technology and Innovation accelerate the achievement of Sustainable Development Goals for All? PLOS Sustain Transform 2(10): e0000085. https://doi.org/10.1371/journal.pstr.0000085

Editor: Hélder Spínola, University of Madeira, PORTUGAL

Received: June 8, 2023; Accepted: October 6, 2023; Published: October 30, 2023

Copyright: © 2023 Adenle et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

In 2015, the United Nations (UN) adopted a set of 17 Sustainable Development Goals (SDGs), including goals to address poverty, hunger, health, employment, the environment, and energy [ 1 ]. The SDGs emphasize the organizational operationalization and integration of sustainability and, therefore, address the current and forthcoming stakeholder needs. Hence, ensuring a better and sustainable future for all, balancing the economic, social, and environmental development [ 2 ]. Science, technology, and innovation (STI) are critical to achieving most SDGs, but successfully delivering STI solutions depends on overcoming longstanding challenges. Given the urgent need to address sustainability challenges within SDGs, new STI policies are required to tackle climate change, strengthen food systems, boost economic resilience, and sustain long-term growth, especially in low- and middle-income countries. Contributing to the effective formulation and implementation of STI is critical in this respect. A substantial body of literature exists linking STI’s critical role in society’s prosperity and well-being [ 3 – 7 ]. The importance of accelerating STI delivery to achieve SDGs has been highlighted by policymakers and in the literature as a crucial issue in the wake of the economic, health, and social consequences of the COVID-19 pandemic [ 8 , 9 ].

Over the years, STI policies, which are one of the critical elements for the implementation of the sustainable development agenda, have become more complex partly due to diverse factors that intervene in the process [ 7 , 10 , 11 ]. The dynamics of multi-stakeholder partnerships for implementing STI policies at the global level raise fundamental issues around the innovation system and its approach in national and regional governments. As a result, it reinforces the need to recognize the network of organizations, actors, and individuals and the rules for introducing, disseminating, and exploiting existing or new technology and knowledge [ 12 , 13 ]. This raises concerns about how various actors and institutions can work together to promote the acquisition and diffusion of innovation systems in terms of knowledge and ideas that will help determine the direction, scale, and successful implementation of SDGs. For example, private sector, consumers, academia, national governments and multi-lateral institutions as well as standards, regulations and, practices across different regions shape global debate on STI cooperation for the achievement of SDGs. In this regard, the range and diversity of global efforts to formulate and manage STI policies to achieve SDGs becomes apparent through the assessment of some of principal interests, obstacles, areas of confrontation, and cooperation [ 7 , 10 , 14 ].

The UN has called for global integration of SDGs into STI to enhance economic growth and prosperity around the world [ 15 ]. Addressing part of the challenges in SDGs requires effective implementation of an STI system in low-and middle-income countries. Evidence shows that the wealthiest countries dominating global research and development have strengthened their economies and suited their production and consumption patterns for several decades rather than meeting global social needs [ 16 , 17 ]. This is reflected in massive research and development investments that have shaped global agriculture, pharmaceutical, transport, energy, infrastructure, and other economic sectors. Technology has revolutionized how these industries conduct their businesses and enabled businesses to foster product development, diversification, and market penetration. Indeed, technology ownership dominated by high-income countries has raised many questions about whether low-and-middle-income countries are paying enough attention to technology development underpinned by science and innovation to promote economic growth and solve social problems. The dominance of a few companies in a global market is partly due to their ability to harness science and innovation to develop new technology. This is particularly true as technologies that could benefit social development are frequently held by the private sector in high-income countries. For example, the private sector accounts for more than 66% of global research and development spending, primarily targeted toward narrow goals and business interests in high-income countries [ 18 ]. The fact that the private sector controls intellectual property rights makes access to technologies for social benefits a daunting task [ 19 ]. Limited access to technology has been argued to be one of the key reasons for inequality between high, low-and-middle income countries [ 20 ]. It also presents a significant challenge to the achievement of SDGs. In light of this challenge, technology and the combination of STIs can play an important role in the achievement of the SDGs. However, how STI is prioritized in national innovation systems to achieve the SDGs, especially in low-and-middle-income countries, remains unclear. In this regard, the inclusiveness of technology-enabled solutions is critical to addressing agenda 2030 particularly, global production, consumption, food insecurity, poverty and climate change [ 15 , 21 ].

Evidence suggests limited participation of the global south scientific community in formulating SDGs. This has been attributed to a widening gap of science and research between high and low-and-middle-income countries. For example, in 2014, the OECD countries accounted for more than 3500 researchers per million inhabitants when compared to 70 per million inhabitants in high income countries [ 18 ]. African countries remain at the bottom of the table with only 2.6% contribution of total scientific publications in 2014. This shortcoming prevents them from active engagement at the international level due to lack of knowledge-based and context-specific transformation pathways. Moreover, inadequate investment in research and development might have not only contributed to limited engagement in formulation of the SDGs and the ability of low-income countries to realize SDGs. Yet, the UN system continues to call for the increased participation of low-and middle-income countries to ensure successful implementation of SDGs.

An international interdisciplinary team has identified critical elements of national and international policies and strategies to deliver SDG STI solutions. The aim was to create a framework that fosters a systemic approach to planning and undertaking the required actions to facilitate the work of national governments and international development agencies globally. To the best of our knowledge and reach, only a few studies have explored interdisciplinary STI in the context of achieving SDGs and have directly addressed research questions around: 1) the integration of SDGs into STI cooperation; 2) the need for increasing levels of participation by the global South scientific community in the implementation of SDGs; and 3) STI-based solutions relevant to the achievement of the SDGs.

To this end, we assessed the perspectives of relevant stakeholder groups (i.e., policymakers, academia, donor, private sector and non-governmental organizations) through an international survey (see methods ) on the role of STI in tackling SDG challenges in three main themes: 1) agriculture (SDG1 and SDG2—no poverty and zero hunger), 2) health (SDG3—ensure healthy lives and promote well-being for everyone of all ages), and 3) environment and energy (SDG7—ensure access to affordable, reliable, sustainable and modern energy, and SDG13—take urgent action to combat climate change and its impacts).

Objectives and Research Design

This study examines the role of Science, Technology and Innovation (STI) and the factors that facilitate and/or hinder the effective implementation of SDGs, especially in Low- and Middle-Income Countries (LMICs). According to the World Bank’s fiscal year 2024 classification, LMICs have a Gross National Income (GNI) per capita between $1,136 and $4,465 [ 22 ]. These countries are generally in a transitional phase, experiencing some economic development and improvement in living standards.

To achieve the study objectives, a two-stage approach was applied: (1) a survey with stakeholders and (2) the development of a framework.

• (1) Survey with stakeholders

Questionnaire development and measurement scales

First, based on meetings, experience-sharing of the authors as ad hoc experts in the field, related reports [ 23 , 24 ], and literature review [ 25 – 27 ], a questionnaire was developed to capture the main obstacles, challenges, solutions, and reinforcing or conflicting mechanisms on how STI can be applied to achieve SDGs. The 2030 Agenda highlights the integrated nature and indivisibility of the 17 sustainable development goals; yet acknowledges that different countries set their priorities according to their national context. Hence, SDG1 (no poverty), SDG2 (zero hunger), SDG3 (health and well-being), SDG7 (energy), and SDG13 (climate change) were selected in this study, considering their shared relevance and the positive impacts STI can bring to their implementation in LMIC (UN Note SDGs VNRs) [ 28 ]. Furthermore, previous research highlights that connections between the SDGs must be identified and tackled, increasing the need for partnerships and effective collaboration between different stakeholder groups, as SDG17 aims for [ 2 ]. Moreover, SDGs were centered on three main themes: 1) agriculture (SDG1 and SDG2- no poverty and zero hunger), 2) health (SDG3—ensure healthy lives and promote well-being for all at all ages), and 3) environment (SDG7—ensure access to affordable, reliable, sustainable and modern energy and SDG13—take urgent action to combat climate change and its impacts). These themes were recently suggested in a book that covers many pressing problems and current opportunities, emphasizing the role of STIs in developing countries. The book was edited by researchers from the team of authors and compiled 26 chapters written by 71 authors from 18 countries (Adenle et al., 2020) [ 25 ].

The questionnaire was developed in English and structured in the following sections: i) socio-demographic information of the respondents; ii) obstacles in the application of STI and challenges in the STI cooperation between low-middle and high-income countries; iii) solutions to achieve SDGs 1, 2, 3 (including COVID-19 recovery), 7 and 13; and finally, iv) reinforcing/conflicting interactions between STI interventions and the achievement of SDGs (e.g., cheap energy can increase the access to basic services, but also for additional demand, generating negative environmental impacts).

It is important to determine respondents’ perceptions on the obstacles, challenges, and solutions of STI given the relevance to the SDGs. Researchers have used a similar approach to explore the link between obstacles/challenges and solutions to gain insights into the implementation of new policies [ 26 , 27 ]. To assess the responses, 5-point Likert scales (Importance) were applied, ranging from (1 = Unimportant, 2 = Of little importance, 3 = Moderately important, 4 = Important, and 5 = Very important); the mean values of each scale item are presented in the results. The internal consistency of the scales was measured with Cronbach’s Alpha [ 29 ]. Cronbach’s alpha tests to see if the multiple-question Likert scale surveys are reliable. It is a measure of internal consistency, i.e., how closely related a set of items are as a group. It is a function of the number of test items and the inter-correlation among the items. In this study, the scales measuring Obstacles (0.825); Challenges (0.777); Solutions for SDG1 (no poverty) and SDG2 (zero hunger) (0.819); Solutions for SDG3 and COVID-19 recovery (0.804); and Solutions for SDG7 (energy) and 13 (climate change) (0.819) obtained satisfactory reliability values (>0.7) as described by Hair and his group [ 30 , 31 ].

Sampling and data collection

The online survey was conducted between July 2021 and February 2022, and a stakeholder-based survey approach [ 26 ] was used. This means that information from a broad range of stakeholder groups (including academic researchers, policymakers, donors, agents of the private sector, and non-governmental organizations) around the world was collected. It is assumed that those multiple actors who are actively involved in the STI and SDG debates are also well-informed and aware of the challenges LMICs face. Moreover, it is possible to go beyond simple questions designed for citizens who do not feel familiar with the issue. Finally, analyzing stakeholders’ perceptions can lead to strategic decisions in the public and private spheres to support STI and the successful implementation of SDGs in LMICs.

Survey invitations were distributed to the professional networks of the interdisciplinary study team via e-mail and relevant social media websites (Facebook, Twitter and LinkedIn). These social media networking sites also extended generic invitations, personal invitations, and group invitations. All invitations specifically included the request to share and further distribute the invitation with additional professional networks. The snowball method was used to reach more targeted respondents [ 32 ].

Online surveys commonly face selection and respondent bias where the population to which they are distributed cannot be described, and respondents with biases may select themselves into the sample [ 33 ]. To mitigate the challenges, surveys were targeted through professional networks. The invitations consisted of a brief outline of the study and who was required and had the profile to complete it. The online questionnaire was anonymous, following the ethical approval guidelines, and did not ask for personally-identifiable information. The research team acknowledges that sampling bias does not allow the statistical generalization of the results. Yet, through convenience and purposive sampling, findings relevant to a sub-population of interest (in our case, experts and stakeholders with knowledge of STI and SDGs) could be identified.

In total, 199 responses were collected from questionnaires distributed around the world. The survey was programmed to require the respondents’ consent to participate in the survey and only forms that included answers for all the required questions could be finalized and submitted to the server. It is estimated that the survey reached out to more than 1,000 academic researchers and more than 500 different stakeholder groups including the private sector, NGOs and donors. This represents a response rate of approximately 13% which is considered acceptable for surveys online [ 34 ]. To reduce the response bias (i.e., situations where people do not answer questions truthfully for some reason), the study followed best practice recommendations (e.g., the survey was short and to the point to avoid respondent fatigue, the language was unambiguous and the questions were interesting and relevant to the respondents, keeping them engaged). To reduce non-response bias (i.e., when those unwilling or unable to take part in a research study are different from those who do), the team reinforced the contacts with key stakeholders and kept the data collection flexible (via emails and social media, snowball sampling), but focused on the target group [ 35 ]. It is important to acknowledge that STIs can facilitate the achievement of SDGs in different countries, but the challenges and obstacles are higher for LMICs. For that reason, the survey was distributed in a systemic way to the target sample, (i.e., irrespective of the country of origin of the stakeholders). Future research might explore the perception of stakeholders located in LMICs or High Income Countries, for example, since results could provide different perspectives and segmented strategies for the achievement of the SDGs.

The final sample was composed of 199 participants (52.8% male and 47.2 female, 81.4% postgraduate, 40.7% ranging from 36–49 years old) belonging to relevant stakeholder groups (e.g., academia (42.2%), international (13.6%) and non-governmental organizations (18.6%), policymakers (15.1%), donors (1%)).

• (2) Framework Development

The second methodological approach is based on the assumption that the SDG framework lacks a holistic approach that recognizes the interconnectedness among its goals and targets. Indeed, progress toward one goal could either impede or enhance progress towards other goals, as described by previous studies [ 61 , 62 ]. To address the challenges and complexities of integrating Science, Technology, and Innovation (STI) into SDG achievement, a comprehensive framework is proposed, focusing on four key dimensions: i) national planning; ii) resource and capacity building; iii) engagement and partnerships, and iv) access. These dimensions are substantiated by the literature [ 63 – 65 ] and by the viewpoints of the authors. Data from the stakeholders’ survey (as described beforehand) encompassing a broad spectrum of STI-related issues are crucial for attaining the SDGs and advancing towards an overall sustainable development agenda, all of which complements the framework development. The framework will be presented in detail in the next section.

Ethics approval

Ethics approval (2021-IRB16) was granted by the Institutional Review Board of the International Center for Tropical Agriculture (CIAT). For inclusion in the study, respondents were required to complete three levels of voluntary consent: (i) the participant agreeing to participate after reading the purpose and nature of the study, (ii) the participant granting permission for the responses to be used in research publications; and (iii) the participant granting permission for the research to use direct or attributed quotations from the interview.

Factors hindering STI applications in low- and middle-income countries

Two sets of questions were applied to better understand the factors hindering STI applications in low- and middle-income countries.

First, survey respondents were asked to rate the importance of obstacles ( Fig 1A ) in applying STI in achieving SDGs. As described in Fig 1A , seven obstacles were considered to be very important. Nevertheless, “poor infrastructure” (including “agriculture and food distribution, and health systems”) was considered the most important obstacle in applying STI to achieve SDGs, followed by “constraints and limitations related to cooperation of the global scientific community”.

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(A) Current obstacles in applying STI to achieving SDGs in low- and middle-income countries. Bars represent mean scores, based on a 5-point Importance (Likert) Scale. (B) Current challenges in achieving cooperation between high income and low- and middle-income countries to provide STI solutions for SDGs. Bars represent mean scores, based on a 5-point Importance (Likert) Scale.

https://doi.org/10.1371/journal.pstr.0000085.g001

Next, respondents were asked to indicate the degree of importance of different factors to overcome some of the challenges while stimulating the cooperation between high-income and low- and middle-income countries in providing STI solutions for SDGs ( Fig 1A ). They indicated that limited participation of the global south scientific community, limited public-private partnerships, lack of interdisciplinary, absence of indigenous knowledge in the formulation of SDGs, and absence of frugal innovation for the basis of the pyramid, as hurdles for the application of STI in LMICs. These instances demonstrate the increasing need to integrate the global scientific community’s perspectives into the decision-making process for cooperative STI policy [ 7 ].

To overcome these barriers and stimulate cooperation between high-income and low- and middle-income countries in providing STI solutions for SDGs, respondents indicated education and human capital as the most important factors followed by policies and governance, infrastructure and distribution systems (agriculture, food, and health). Our findings are consistent with evidence provided by recent literature on these topics, concerning the significant role played by these factors on global development and SDGs. Concerning education and human capital, Angrist et al (2021) [ 36 ], Pubule et. al (2021), [ 37 ] and Ryymin (2021) [ 38 ] provide evidence supporting our results and the need for an integrated approach to these topics, supported by innovation frameworks, digitalization, knowledge governance strategies and knowledge-sharing systems. Institutional capacity, finance, and international market/trade were ranked as relatively less important, although all factors are considered important ( Fig 1B ).

Solutions to achieve SDG1 and SDG2

STI solutions such as improved infrastructure, agriculture, and food distribution systems in low- and middle-income countries (representing roads, ICT, post-harvest technologies, etc.) obtained the highest degree of importance for SDG 1 and SDG 2, followed by equitable access to innovation to fight poverty and strong national innovation systems ( Fig 2 ). Prioritization of local innovation also obtained the same importance for SDG1 and SDG2, while free access to intellectual property received a much lower score.

Bars represent mean scores, based on a 5-point Importance (Likert) Scale.

https://doi.org/10.1371/journal.pstr.0000085.g002

Our findings agree with recent literature data on ICT and post-harvest technologies concerning the major role played by these factors on global development, sustainability and achievement of SDGs, especially with respect to SDG1 and SDG2. Evidence from these studies corroborate our findings and indicate the critical role played by local innovations [ 39 , 40 ] and the presence of good infrastructure for improving sustainability practices in development projects both in agriculture, supporting food distribution systems [ 41 ]) and health, supporting distribution and availability of vaccine and drugs [ 42 – 44 ].

Solutions to achieve SDG3 and COVID-19 recovery

The same approach was applied in the health section to assess how STI solutions are relevant to achieving SDG3 targets and COVID-19 recovery. The importance of six solutions is shown in Fig 3 below.

https://doi.org/10.1371/journal.pstr.0000085.g003

Equitable access to innovation (vaccines, diagnostics) was considered the most important factor in achieving SDG3 and COVID-19 recovery, followed by affordability and distribution of vaccines and other medical products, proper infrastructure for rapid scaling and distributing medicines, vaccines, and food to the population (e.g., cold storage facilities in remote areas) and low-cost healthcare innovation ( Fig 3 ). As reported by Lehoux et al (2015) [ 45 ], our findings further underscore the need to address equity and sustainability challenges as related to lack of access to health innovation (including vaccines and diagnostics) and limited infrastructures that are resulting to poor delivery of healthcare services in LMICs. The cost-effectiveness of the innovation and free access to intellectual property were rated lower relative to the other issues, but were still deemed important (above 4). The fact that the two factors were deemed important suggests that the STI approach to health innovation is primarily driven by dominant multinational IPR and speculative investment where short-term profits/returns supersede long-term health gains and well-being of society in poor countries [ 45 , 46 ].

The importance of accessing innovations and complementary solutions in strengthening the distribution infrastructure is critical in achieving SDG3. A reliable supply chain infrastructure in conjunction with information systems would facilitate rapid scale-up of clinical solutions and relevant knowledge of safe and effective use of STIs at the local levels. Furthermore, for affordable STI solutions to be developed, additional effort is required to improve the capacity to generate evidence at multiple levels from evaluating the STI with respect to safety, equity, and local adaption to optimize the potential benefit at the community level. Hence, the improvements and alignments of digital and transport distribution infrastructure are equally important [ 47 ]. Investment in improving generic digital and health literacy at the local community level would enhance the readiness for rapid scale-up of STI solutions such as COVID-19 testing and vaccine rollout.

Solutions to achieve SDG7 and SDG13

With respect to the environment and energy sector, respondents evaluated the role of STI in achieving SDG7 and SDG13. Access and affordability of clean technology fuels obtained the highest mean score rating, followed by the development of human capacity for the development and deployment of clean and affordable energy technologies and low-cost technologies ( Fig 4 ). Renewable energy sector is strongly connected with climate change mitigation and adaptation and numerous literature evidence have established their correlation and interdependency, especially in terms of research and development, access and affordability of clean technologies. For example, the IPCC report [ 48 ] argues the that lack of R&D represents a significant challenge to deployment of clean technologies in low-and middle-income countries, which is in line with our findings. Financing was also a major factor driving STI in meeting SDG7 and SDG13, though considered slightly less important than the top three in implementing the sustainable development agenda. Yet, weak financing instruments undermine the implementation of low carbon technologies in LMICs [ 49 , 50 ]. Flexible access to IP obtained the lowest mean score, but once again, all factors were considered important (above 4.0).

Bars represent mean score, based on a 5-point Importance (Likert) Scale.

https://doi.org/10.1371/journal.pstr.0000085.g004

While LMICs have increased R&D expenditures over the past two decades, these are still relatively low in many countries. For example, Sub-Sahara Africa invests an average of 0.25% of GDP in R&D programs despite recommendations by the Africa Union to invest at least 1% [ 51 , 52 ]. Without substantial financial resources, LMICs cannot implement green economy projects (SDG7) or perhaps meet the Paris Climate Agreement (SDG13). While the importance of STI for achieving the SDGs is well-recognized in high income countries, STI policy applications to support sustainable development goals are limited in many developing countries due to financial and structural impediments [ 53 , 54 ].

Intended and rebound effects

The role of STI in mediating SDG linkages remains mostly unexplored. Behavioral and systemic responses that counteract environmental gains from technological change, so-called rebound effects, could expose hidden barriers toward an internally consistent application of the SDG framework. For example, the linkages between energy, poverty, and climate goals are mediated via energy efficiency improvements [ 55 ]. A better understanding of rebound effects could improve our understanding of STI-mediated SDG interlinkages, investigate the SDG framework’s consistency as a whole, and assist in identifying appropriate management strategies to mitigate undesired effects.

This is in line with the recommendations of the International Council for Science and the International Social Science Council (2015) to associate goals with specific resource intensity targets [ 56 ]. To capture stakeholders’ views and guide future research, respondents were also asked to rate the importance of various STI-mediated reinforcing (mutually beneficial) and conflicting (mutually hindering) interactions between individual STI interventions to achieve the SDG framework. Interactions focused on rebound effects, often observed in policies aimed to increase the efficiency of utility services (e.g., energy and water efficiency) and land/agriculture productivity [ 57 ].

For each topic, reinforcing interactions were generally rated higher than conflicting ones across demographic groups while also describing the highest variation or lower consensus ( Fig 5 ). Our results describe a stark contrast with the focus of the rebound effect literature, which is dominated by conflicting interactions on energy efficiency and energy in general [ 58 ]. Such interactions are perceived among the least important (average score of 3.5 for energy efficiency) while also describing the highest asymmetry between conflicting and reinforcing mechanisms. The detrimental effects of energy inefficiency on energy use and related carbon emissions is also widely acknowledged as critical to sustainable development and environmental policy effectiveness [ 57 , 59 ]. Our results may suggest that, rather than being insignificant, this particular topic may be factored in. In other words, the current body of evidence is extensive and research on this topic already yields low marginal returns in this context. In addition, the mainstream research focuses on the detrimental effects from energy efficiency, which may be perceived as insufficient to unravel the complex interactions that impede the achievement of the SDG framework. Demographic variables cannot be disregarded as mainstream research is largely carried out by males from academic institutions in high-income countries. In line with recent calls for a greater alignment with sustainability science [ 60 ], and with the help of stakeholders’ views, greater efforts must be redirected towards reinforcing interactions to unveil welfare-enhancing effects as well as overlooked topics such as agricultural land productivity and increased water use efficiency. Clear goals and metrics should be established, and activities planned for each STI initiative to improve clarity and accountability.

https://doi.org/10.1371/journal.pstr.0000085.g005

From the outset, the SDG framework lacked a systems approach that addresses the inherent linkages across goals and targets. In fact, progress towards one goal could either hinder or reinforce progress towards other goals [ 61 , 62 ] as supported by stakeholders’ viewpoints.

Below, we present a framework for STI cooperation for the SDGs focusing on four dimensions: national planning, resource and capacity building, engagement and partnerships, and access to innovation to deal with the challenges and issues in achieving the SDGs ( Fig 6 ). These dimensions are elaborated in Table 1 and also touch on significant aspects of each dimension that can foster STI cooperation. Further, these dimensions reflect the authors’ viewpoints supported by survey data, which are described across a wide range of STI issues for achieving SDGs. The dimensions highlight the need to gain insights into potential multi-sectoral inter-linkages across SDGs to enhance cooperation among various actors at the national or global level. These insights would be a prerequisite for understanding broader consequences relating to the application of STI and collective action in evaluating them. With this approach, an integrated natural and social sciences will be useful to policymakers, the research community, the business community and the broader society in innovation thinking, interactive design, transition management, awareness creation and responsible scaling as well as multidisciplinary sustainability science for the achievement of the sustainable development agenda such as SDGs [ 63 – 65 ].

Source: authors.

https://doi.org/10.1371/journal.pstr.0000085.g006

https://doi.org/10.1371/journal.pstr.0000085.t001

Discussion and conclusion

Our findings reveal some fundamental challenges that could undermine the effective implementation of SDGs in LMICs. Challenges such as the lack of proper engagement of the global south scientific community and lack of interdisciplinary are hurdles that should be addressed to foster STI collaboration between the global South and North. Identifying and prioritizing SDG interactions require scientific collaboration through an interdisciplinary approach [ 66 ]. The global community should encourage scientific research partnership opportunities for the global South to unlock transformational capacity of science, research and training while sharing its gains equitably. It is important to encourage interdisciplinary research partnership that allows contributions from natural sciences, social sciences, life science and engineering among others. SDGs will not be achieved without emphasizing the importance of interdisciplinarity and scientific engagement within STI systems at the national and international levels [ 18 , 66 ].

Indigenous knowledge and frugal innovation could be recognized as critical components of STI towards the achievement of SDGs in low-and-middle income countries. Moreover, the fact that indigenous knowledge and frugal innovation do not reflect the broader objectives of the SDGs have drawn criticism from various key stakeholders [ 56 , 67 – 69 ]. This essentially means that the way in which either local knowledge or frugal innovation is perceived has not received much-needed attention from the key actors especially the UN body, international donors and the private sector. The failure to recognize that development needs must take into consideration Indigenous peoples’ experiences in LMICs may undermine the achievement of SDGs at the local level. Our study is in agreement with Cummings (2017) who argued that “the SDGs are fundamentally flawed because they are not based on local realities and local knowledge”[ 70 ] p22). Furthermore, the lack of specific mentions of frugal innovation in the formulation of SDGs suggests that its contributions to sustainable development in rural areas may be overlooked. Yet, frugal innovation offers a promising approach for sustainable development through frugal enterprises and economic growth as well as social-influence impacting aspects such as poverty and health concerns. A growing body of literature has documented the critical role of frugal solutions in terms of sustainable enterprise, business model, supply chain management and environmental sustainability at the grass root level in low-and-middle income regions [ 71 , 72 ]. The authors argued that frugal innovation such as low-cost technologies can deliver affordable solutions to consumers in important sectors including agriculture, education and health. In light of this potential, it is therefore important to harness the transformational role of local knowledge and local innovations to achieve the SDGs.

The role of ICT and agricultural technologies as relevant STI solutions cannot be overemphasized in improving agricultural productivity and the food system, while tackling poverty in low-and-middle income countries. Currently, inappropriate land use and food systems are contributing to persistent hunger, malnutrition, and obesity [ 73 ], with poverty being a leading cause of persistent hunger. At the same time, agriculture and food systems underpinned by STI present a means to reduce poverty and ensure sustainable, safe, equitable and healthy diets for all [ 25 ]. Several studies indicate that new technologies and innovations are required to transition to sustainable agriculture and food systems to achieve economic growth, and human and planetary health [ 25 , 74 – 76 ]. The importance of STI solutions in strengthening the agricultural and food system is vital for escaping poverty (SDG1) in agriculture-based economies such as sub-Saharan Africa and South Asia. As noted in the era of green agricultural technology revolution, STI approaches such as improved infrastructure and national research and development programs tailored towards high-yielding varieties helped efforts in hunger and poverty reduction in Asia [ 77 ]. A strong R&D system supported by STI competencies calls for planning development projects that mainstream SDG2 into national innovation policies in low- and middle-income countries. Therefore, integrating STI policy into national development planning in relevant sectors is critical to achieving the SDGs.

The STI cooperation between high-income and low- and middle-income countries underscores the role of STI in achieving the SDGs in agriculture, health, environment, and energy systems using key indicators to solicit viewpoints from different stakeholder groups. The survey respondents considered education and human capital (4.86 out of 5) as the most significant factor for STI cooperation, followed by policies and governance (4.68) and infrastructure (4.66). Respondents’ viewpoints suggest that interventions in science and innovation skills and industrial policies are limited in most low- and middle-income regions. The fact that education and human capital were best rated for STI cooperation among respondents means that national and international governments need to pay adequate attention on investments in human capital development, research and acquisition of basic skills, to foster effective implementation of SDGs. Specifically, national governments should promote the creation of opportunities for industries by supporting scientific R&D for innovation-driven economies, and build their abilities through education and training policies. The research gap between high-income and low- and middle-income countries reflects the underlying challenges for international STI cooperation. The fact that STI human resource capacity is still very weak in many developing countries, even in the middle-income ones, suggests that achievement of SDGs 1, 2, 3, 7 and 13 will require strengthening the educational system, increasing investment in R&D programs and providing adequate infrastructure and expertise for the requisite skills and competencies to foster STI cooperation. According to Fonseca et al. (2020), SDG1 (poverty elimination), and SDG3 (good health and well-being) have synergetic relationships with most of the SDGs, while SDG7 (affordable and clean energy) has a significant relationship with other SDGs (e.g., SDG1, SDG2 (zero hunger), SDG3, SDG8 (decent work and economic growth), and SDG13 (climate action)). Hence, effective measures for advancing the SDGs and, ultimately, sustainable development for all demands that the relationships and interactions between the SDGs must be identified and tackled. In this sense, the discussion on conflicting and reinforcing effects following the rebound effect framework and the body of work sheds relevant insights for further research and ultimately towards an effective implementation of the SDG framework. Furthermore the SDGs’ synergies and trade-offs represent an opportunity for policy and decision-makers by suggesting that the frequently linear development paths of economic growth ahead of social equity and environmental protection might be challenged by other systematic approaches that offer multiple solutions and drivers for different contexts.

In this regard, the four key components of the framework ( Fig 6 ) will require many low- and middle-income countries, if not most, to undertake a shift in public policy at the national level for STI cooperation. Achieving SDGs in many low- and middle-income countries will depend on both domestic innovation and catching up technologically to position their national innovation system and policy environment to take the best advantage of knowledge transfer at the global level for STI cooperation.

For STI cooperation to happen, the transformation of industry, universities and research institutes in low- and middle-income countries is crucial to complement mutual strengths and a win-win approach in key areas, such as institutional policy and governance structure, higher level of scientific training, entrepreneurship, research infrastructure, legal framework and technology transfer.

STI solutions for SDGs should be created, including co-funding of R&D by international donors and national governments and effective protection of intellectual property rights, but not to the point where it restricts access to frontier technologies and discourages private sector innovation. A committed and systematic approach can overcome technical, legal, and institutional barriers related to financing STI solutions in low- and middle-income countries.

Capacity-building programs for STI need to be implemented at the sub-national, national, and regional levels, aiming for whole-of-country capacity-building rather than narrow, single-industry approaches. Further, the aim should be to build the necessary capabilities for novel developments, not just the capacity to utilize existing technologies. Complementary investments in educational and research infrastructure are crucial.

Our survey findings and allied research indicate that Science, Technology, and Innovation can be powerful tools to reduce social exclusion and extreme poverty in the world, and are much needed, for example, in the current COVID-19 pandemic. STI can also contribute to improved quality of and access to public services. Although scientists have historically made significant contributions to innovation and technological development of processes and products, only a small part of these contributions have effectively been incorporated into global social welfare. This scenario urgently needs to be changed. New STI governance structures, policy strategies and incentives for Southern scientists are required to successfully integrate STI policy into social policies.

Limitation and future research

The study is beyond the scope of assessments of the impacts of STI for achieving SDGs in low-and-middle income countries as the study only focuses on the viewpoints of selected stakeholder groups around the world. The viewpoints of these stakeholders are not representative of all, which is one of the limitations of this study. In addition, almost 200 respondents were analyzed across thematic topics so it is not possible to generalize the results. Given the importance of this study and its contribution to the literature on STI/SDG, it is worth assessing the impacts of STI towards achieving SDGs at the national and regional levels. In this regard, this study can help gain insight into the role of each component in tackling the challenges in SDGs and interactions across different goals, while considering the synergies and trade-offs in the process leading to the achievement of SDGs.

Acknowledgments

Special thanks to Africa Sustainability Innovation Academy in leading and coordinating the research. Authors would like to express their appreciation to Cyrus Muriithi (Alliance of Bioversity International and CIAT) for the support in programming and administering the online survey. We thank Prof. Adelaide Antunes and Dr. Alessandra Moreira de Oliveira (Federal University of Rio de Janeiro and the Brazilian National Institute of Industrial Property) and Dr. Akira Homma (Bio-Manguinhos, Oswaldo Cruz Foundation) for their valuable support in coordinating the application of the questionnaires in Brazil. Authors are grateful to the editor and two reviewers in providing constructive comments that have improved the quality of the final manuscript.

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The Role of Science, Technology and Innovation in Building Resilient Communities, Including Through the Contribution of Citizen Science

People around the world are continuously affected by shocks; from health emergencies to economic crises, from social conflicts and war to natural disasters. Moreover, global economic interdependence creates increasingly complex and unpredictable threats that can derail the progress towards the SDGs. Therefore, building resilience is critical for sustainable development.

Resilient communities empower their people to absorb and adapt to shocks, have economies that can self-organize to continue functioning at times of crises, and carry out their activities without harming the environment.

Science, technology and innovation have a critical role to play in each one of these dimensions. Digital technologies have empowered and given voice to people; innovation results in economic diversification, which increases the ability of economies to adapt to shocks; and new technologies are used for resource management and could help to decouple economic development from environmental degradation.

A new development is citizen science, which uses the latest technologies to engage volunteers to carry out tasks such as data collection in support of science.

The report discusses key challenges on STI for resilient communities. Technical challenges are related to data and underlying enabling technologies, and the need for prudent use of data acquired during citizen science projects. Social challenges are related to knowledge generation and use, considering that resilience is not neutral but reflects social norms and competing interests within the community. Market challenges are related to scalability and sustainability of technological solutions for community resilience beyond the prototyping phase. Another critical issue is the need to develop STI solutions that are resilient themselves, given that disruption could be extremely harmful to the communities.

The report underscores the critical role of international cooperation and presents policy suggestions for governments to promote science, technology and innovation towards community resilience.

• Science, Technology and Innovation - Current Issues

Essays on Science and Innovation

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The commercialization of scientific discoveries into innovation has traditionally been the purview of large corporations operating central R&D laboratories through much of the past century. The past four decades have seen this model being gradually supplanted by a more decentralized system of universities and VC-backed startups that have displaced large corporations as the conductors of scientific research. This dissertation tries to understand how firms create and exploit scientific knowledge in this changing structure of American innovation. The first study examines how scientific knowledge can expand markets for technology and thereby encourage the entry of new science-based firms into invention. The argument is tested in the context of the U.S. patent market and finds that patents citing scientific articles tend to be traded more often, even after controlling for various proxies of patent quality. The second study explores why some American firms started investing in scientific research in the early twentieth century. The chapter relies on a newly assembled panel dataset of innovating firms consisting of their investments in science, patenting, financials and ownership between 1926 and 1940. The empirical patterns reveal that the beginnings of corporate research in America were driven by companies at the technological frontier attempting to take advantage of opportunities for innovation made possible by scientific advances. This investment was especially pronounced for firms based in scientific fields that were underdeveloped in the United States. The final study asks why startups are more likely to bring scientific advances to market. The existing literature has explained the higher innovative propensity of some startups by their superior scientific capabilities. However, it is also possible that the apparent innovativeness of startups may be a result of firm choice, rather than inherent capability gaps with respect to incumbents. Startups may choose novel products that are riskier but offer higher payoffs because they pay a higher entry cost in the form of investments in new factories, sales and distribution channels. I test this entry cost mechanism in the context of the American laser industry which responded to an exogenous influx of Soviet laser science following the end of the Cold War.

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Suh, Jungkyu (2022). Essays on Science and Innovation . Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/25166 .

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Dukes student scholarship is made available to the public using a Creative Commons Attribution / Non-commercial / No derivative (CC-BY-NC-ND) license .

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What Is the Knowledge Economy?

• Knowledge and Human Capital

The Bottom Line

What is the knowledge economy definition, criteria, and example.

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and behavioral finance. Adam received his master's in economics from The New School for Social Research and his Ph.D. from the University of Wisconsin-Madison in sociology. He is a CFA charterholder as well as holding FINRA Series 7, 55 & 63 licenses. He currently researches and teaches economic sociology and the social studies of finance at the Hebrew University in Jerusalem.

The knowledge economy is a system of consumption and production based on intellectual capital. It refers to the ability to capitalize on scientific discoveries and applied research.

The knowledge economy represents a large share of the activity in most highly developed economies . A significant component of value might consist of intangible assets  in a knowledge economy, such as the value of its workers' knowledge or intellectual property.

Key Takeaways

• The knowledge economy describes the contemporary commercialization of science and academic scholarship.
• Innovation in the knowledge economy based on research is commodified via patents and other forms of intellectual property.
• The knowledge economy lies at the intersection of private entrepreneurship, academia, and government-sponsored research.
• Knowledge-related industries represent a large share of the activity in most highly developed countries.
• A knowledge economy depends on skilled labor and education, strong communications networks, and institutional structures that incentivize innovation.

Investopedia / Tara Anand

Understanding the Knowledge Economy

Developing economies tend to be heavily focused on agriculture and manufacturing. Highly developed countries have a larger share of service-related activities. This includes knowledge-based economic activities such as research, technical support, and consulting.

The knowledge economy is the marketplace for the production and sale of scientific and engineering discoveries. This knowledge can be commodified in the form of patents or other intellectual property protections. The producers of this information such as scientific experts and research labs are also considered to be part of the knowledge economy.

The Bayh-Dole Act of 1980 was a major turning point in the treatment of intellectual property in the U.S. It allowed universities to retain title to inventions or discoveries made with federal R&D funding and to negotiate exclusive licenses.

The world economy has become more knowledge-based thanks to globalization, bringing with it the best practices from each country's economy. Knowledge-based factors also create an interconnected and global economy where human expertise and trade secrets are considered important economic resources.

Generally accepted accounting principles  (GAAP) don't allow companies to include these assets on their balance sheets, however.

The modern commercialization of academic research and basic science has its roots in governments seeking military advantage.

Knowledge Economy and Human Capital

The knowledge economy addresses how education, knowledge, and " human capital " can serve as a productive asset or business product to be sold and exported to yield profits for individuals, businesses, and the economy.

This component of the economy relies heavily on intellectual capabilities rather than natural resources or physical contributions. Products and services that are based on intellectual expertise advance technical and scientific fields in the knowledge economy. This encourages innovation in the economy as a whole.

The World Bank defines knowledge economies according to four pillars:

• Institutional structures that provide incentives for entrepreneurship and the use of knowledge
• Availability of skilled labor and a good education system
• Access to information and communication technology (ICT) infrastructures
• A vibrant innovation landscape that includes academia, the private sector, and civil society

Example of a Knowledge Economy

Academic institutions, companies engaging in research and development (R&D), programmers developing software and search engines for data, and health workers using digital data to improve treatments are all components of a knowledge economy.

These economy brokers pass on the results of their research to workers in more traditional fields such as farmers who use software applications and digital solutions to manage their crops more effectively. These fields also include advanced technological-based medical procedures such as robot-assistant surgeries and schools that provide digital study aids and online courses for their students.

How Big Is the Knowledge Economy?

It's difficult to put a price tag on the global knowledge economy because it's not a clearly defined category such as manufacturing. It's possible to gain a rough estimate by gauging some of the major components of the knowledge economy, however.

The total intellectual property market was worth $62.18 billion in the United States in 2023, according to the U.S. Chamber of Commerce. The market size of the country's higher education institutions accounted for $818.6 billion in 2023.

What Are the Most Valuable Skills in the Knowledge Economy?

Higher education and technical training are obvious assets but communication and teamwork are also essential skills for a knowledge-based economy, according to the Organization for Economic Cooperation and Development. It's unlikely that any single knowledge worker can generate groundbreaking innovations alone so these interpersonal and workplace competencies are essential to surviving in a knowledge-based workplace.

Which Country Has the Biggest Knowledge Economy?

Factors of a knowledge economy are measured by the United Nations Development Program's Global Knowledge Index. It replaced the World Bank Knowledge Economy Index after 2012.

This metric scores each country based on "enabling factors" for the knowledge economy. These include education levels, technical and vocational training, innovation, and communications technology. Switzerland was the top-ranked knowledge economy with a total score of 69.1% as of 2023. The next two are Sweden at 68.0% and the United States with a score of 66.9%.

The term knowledge economy describes the commercialization of intellectual pursuits. This type of economy capitalizes on research and scientific discoveries. It’s a significant component of highly developed economies such as Switzerland, Sweden, and the United States but it’s global and not confined to just a certain handful of countries. Intellectual property plays a significant role in a knowledge economy.

Unfortunately, generally accepted accounting principles (GAAP) don’t recognize these assets on companies’ balance sheets so you won’t be able to pinpoint titles to them by doing this type of research.

GovTrack. " H.R. 6933 (96th): Government Patent Policy Act of 1980 ."

The World Bank. " The Knowledge Economy, The Kam Methodology, and World Bank Operations ." Pages 5-8.

IBISWorld. " Colleges and Universities in the US - Market Size (2004-2029) ."

Statista. " Market Size of Intellectual Property Licensing in the United States From 2013 to 2023 ."

OECD. " Competencies for the Knowledge Economy ." Page 1.

Knoema. " Global Knowledge Index ."

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• Published: 26 August 2024

Understanding the influence of business innovation context on intentions of enrolment in master education of STEM students: a multi-level choice model

• Ana-Maria Zamfir 1 ,
• Adriana AnaMaria Davidescu 1 , 2 &
• Cristina Mocanu 1  

Humanities and Social Sciences Communications volume  11 , Article number:  1087 ( 2024 ) Cite this article

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This article studies educational decisions, focusing on intentions of enrolment in master’s education of STEM bachelor students. Integrating human capital theory with concepts of cultural and social capital, we propose a two-level model for the choice of pursuing master’s degrees. First level (individual) includes factors covering individual habitus and organisational habitus (higher education institutions of bachelor students), while the second level (local) reflects the local business innovation environment. The proposed model was empirically tested on data collected from a sample of STEM and non-STEM bachelor students enroled in 10 public universities located in Romania. The results show that STEM students display a higher propensity to enrol in master's education, and the gap between STEM and non-STEM majors varies across regions. We find that educational decisions related to master’s degrees are shaped by local circumstances reflecting the business innovation intensity as more innovative business contexts are less conducive for enrolment of students in master programmes. In addition, the findings of the study show that local circumstances are not independent of the field of study when shaping students’ educational choices, highlighting the complex way in which individual and local levels factors interplay and shape educational decisions. STEM students’ propensity to enrol in master’s degrees is more influenced by the innovative business environment than other students. This study has implications for higher education policy and practice aiming to support longer educational careers in STEM.

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Introduction.

While there is a rich literature on the decision to enrol in higher education, lesser attention has been given to the transition from bachelor’s to master’s programmes (Mullen et al., 2003 ; English and Umbach, 2016 ), including in STEM (science, technology, engineering, and math) education. This paper contributes to the study of educational decisions, analysing the intentions of enrolment in master’s education of STEM bachelor students. The study is focused on the students’ decision of pursuing a master-level degree right after graduating bachelor’s studies. Master’s degrees offer graduates of undergraduate education the possibility to acquire deeper or additional skills and knowledge. Master-level programmes engage students in more advanced research methods and independent study while focusing on a narrower specialisation. It is believed that attaining master’s degrees brings significant individual and social benefits (English and Umbach, 2016 ) and supports scientific and technological advancement (Committee on Enhancing the Master’s degree in the Natural Sciences, 2008 ). Master’s degree attainment is consistently rising and understanding factors that shape the transition from bachelor’s to master’s is becoming increasingly important (English and Umbach, 2016 ). A better knowledge of the mechanisms behind this process is of interest to education researchers and practitioners.

According to Hossler and Gallagher’ ( 1987 ) 3-phase college-going model, the first stage of the educational choice is the predisposition phase in which students decide on pursuing or not a higher-level education programme. Following theoretical models developed for undergraduate enrolment, this paper is focused on the factors influencing the predisposition to pursue master’s degrees among final-year bachelor students in STEM and non-STEM fields. Early studies on graduate education relied on the idea of persistence or retention of students in the education system (Tinto, 1975 ; Pascarella and Terenzini, 1980 ). Developing theoretical models based on the literature on undergraduate college choice, more recent works understand students’ process of choosing to pursue a master’s degree as a new and distinct choice among possible post-graduation alternative options (English and Umbach, 2016 ).

Education decisions are shaped by a dense mix of mechanisms, including what individuals can do, what they want and conditions that shape individual preferences and intentions (Gambetta, 1987 ). Such mechanisms embed a wide range of individual, institutional and economic factors (Table 1 ).

Previous studies found significant heterogeneity among master’s degree students (O’Donnell et al., 2009 ; Jung and Li, 2021 ). On the one hand, demographic and background characteristics such as gender, race and age of the students, as well as their area of residence, influence their enrolment in master’s degrees (Perna, 2004 ; Schapiro et al., 1991 ; Xu, 2014 ; Allison and Ralston, 2018 ; Jung and Lee, 2019 ). For instance, the underrepresentation of women, as well as racial and ethnic minorities in STEM education has been analysed by numerous studies (Espinosa, 2011 ; Batsheva and Boards, 2019 ; McKinney et al., 2021 ). On the other hand, education decisions on whether or not to continue education are influenced by the expectations regarding academic success. Individuals with higher academic performances tend to take greater risks in this matter and enrol in higher levels of education (Latiesa, 1989 ; Mullen et al., 2003 ; Zamfir et al., 2021 ). According to Bourdieu ( 1977 ) theory on the role of cultural and social capital in education, parents’ education influences educational transitions as students with parents having a higher level of education are more likely to graduate at a higher education level (Jiménez and Salas-Velasco, 2000 ; Mullen et al., 2003 ; Zamfir et al., 2021 ). Family income is another factor of influence for educational careers. Students with better economic circumstances are more likely to enrol in higher-level courses (González and Dávila, 1998 ; DeBacker and Routon, 2021 ), including master’s degrees (Schapiro et al., 1991 ). Studies exploring the influence of the level of educational debt on decisions to attend graduate degrees have found mixed results (Schapiro et al., 1991 ; Weiler, 1994 ).

Other factors that shape educational choices are related to educational institutions. Quality and other characteristics of the academic environment influence educational choices and decisions in higher education (Kallio, 1995 ; Zhang, 2005 ; Zamfir et al., 2021 ). Educational careers are also shaped by the satisfaction of students with education. In general, the satisfaction of students with higher education is determined both by the perceived institutional performance and the perceived outcomes of institutional performance (Hartman and Schmidt, 1995 ). Moreover, it has been found that the type of university influences the transition from bachelor’s to master’s as students from research universities are more likely to pursue master’s degrees than those from teaching-oriented universities (Jung and Lee, 2019 ).

Previous research indicates a significant relationship between the field of study and the decision to pursue a master's education. It has been found that students from the arts stream are less predisposed to enrol in postgraduate studies compared to science stream students (Kong et al., 2015 ; Jung and Lee, 2019 ; Zamfir et al., 2021 ). Considering this difference between STEM and non-STEM students, it would be very important to better understand the variation between the two fields of study.

We know that investing in education fosters the accumulation of knowledge and skills, allowing individuals to have access to better job prospects and higher wages (Becker, 1962 , 1990 ; Thomas and Perna, 2004 ; Paulsen and Toutkoushian, 2008 ). According to the economics of education, investing in human capital is motivated by expected economic returns (Becker, 1962 , 1990 ; Thomas and Perna, 2004 ; Paulsen and Toutkoushian, 2008 ; Menon et al., 2017 ). Considering the human capital theory, the demand for higher education has been studied in relation to labour market factors such as the level of unemployment (Becker, 1964 ; Ashenfelter and Ham, 1979 ; Jiménez and Salas-Velasco, 2000 ) and expected earnings upon completion of a degree (Kodde, 1986 ). Expectations regarding monetary and non-monetary returns of education are relevant for educational choices (Altonji et al., 2015 ). Increased expected earnings have been found to positively influence the enrolment demand, particularly for post-graduate education (Handa and Skolnik, 1975 ). Jiménez and Salas-Velasco ( 2000 ) propose a model of factors determining educational choice, which includes objective and subjective determinants related to the current situation of the students, but also to the future, such as employment prospects and expected income. Additionally, students’ participation in the labour market influences their enrolment in master’s degrees (Zamfir et al., 2021 ).

Moreover, differences in pursuing master’s degrees in relation to students’ majors are mainly determined by differences in the expected benefits (English and Umbach, 2016 ; Zamfir et al., 2021 ). In general, STEM degrees bring higher returns (Burgess, 2016 ), encouraging individuals to pursue more education in related fields.

Returns to education are influenced by local conditions of the economic environment. Our previous results showed that local labour markets characterised by higher employment in science and technology increase the probability of bachelor students to enrol in master education. In addition, more dynamic local contexts with respect to earnings level, business demography, and business innovation discourage students from prolonging their education career, probably due to higher forgone earnings (Zamfir et al., 2021 ). From this point of view, it is possible that differences between the intentions of STEM and non-STEM students vary in relation to the local context.

In addition, the theory of skilled-biased technological change (Machin and Van Reenan, 1998 ) highlights the rising demand for higher skills in more technologically advanced contexts. Previous studies linked various proxies of technological change and innovation with a higher demand for skilled workers (Toner, 2011 ). Evidence suggests a virtuous circle between the education and skills of the workforce and business innovation capacity, as enterprises with a higher proportion of more skilled workers have a higher probability of introducing new products (Toner, 2011 ). From this point of view, one could expect contextual factors reflecting the business innovation environment to influence returns to education for STEM and non-STEM students in a different way. Thus, we draw on human capital theory while taking into consideration the business innovation environment for exploring the factors shaping the decision of enrolment in master’s degrees of STEM vs. non-STEM students. Detecting the factors that influence different educational choices of STEM and non-STEM students is useful for identifying effective measures for increasing participation in STEM master education. Promoting STEM education is considered a key element for driving innovation and economic growth worldwide.

Research questions and objective

Integrating human capital theory with concepts of cultural and social capital, Perna ( 2006 ) developed a model of college choice based on four layers: individual habitus, community and school context (organisational habitus), higher education context and socio-economic and policy context. Studying transition from undergraduate to graduate education, English and Umbach ( 2016 ) revise the approach of Perna ( 2006 ) and propose a two layers conceptual model integrating individual and institutional factors that influence students’ decision to pursue graduate education. The individual layer (habitus) includes demographic characteristics, cultural and social capital, academic achievement, supply of resources and expected benefits. The second layer covers features of the undergraduate institution context.

Building on the previous work of Perna ( 2006 ) and English and Umbach ( 2016 ) regarding the multiple layers of factors that influence educational choices, we take into account individual characteristics, as well as features of institutional and business innovation contexts that shape educational choices. Therefore, the objective of this paper is to propose and test a two-level model for the choice of pursuing a master’s degree that integrates individual and institutional factors with circumstances in the local business innovation environment. First level (individual) includes individual habitus and organisational habitus (higher education institutions of bachelor students, including enrolment in STEM vs. non-STEM fields). The second level (local) reflects local conditions concerning the business innovation environment. The second level recognises that the choice of pursuing master’s degree is influenced by wider forces and conditions that interplay with individual factors and shape individual preferences and intentions. The conceptual model of the current study is presented in Fig. 1 .

This figure illustrates the two-level model that integrates individual and institutional factors (level 1) with circumstances related to the local business innovation environment (level 2) influencing the choice of pursuing a master’s degree.

As has been shown before (Zamfir et al., 2021 ), STEM bachelor students display a higher propensity to enrol in master’s degrees than other students. In addition, previous results indicate that a more dynamic business innovation local context discourages students to pursue graduate education. By developing the two-level model for the choice of pursuing a master’s degree, this paper is focused on addressing the following research questions:

RQ1: Do STEM vs. non-STEM majors have different effects on intentions of pursuing master education in different local contexts?

RQ2: Does and how does the business innovation context interplay with STEM vs. non-STEM majors in shaping the predisposition of enrolment in master education?

Data and methods

In order to empirically test the proposed model, we integrated data from various administrative and statistical sources for a sample of students enroled in ten public universities located in Romania (out of a total of 54 public universities). In 2019, intentions of pursuing a master’s degree in the next school year (2020/2021) were collected via a questionnaire-based survey from 502 students enroled in their final year of bachelor studies (age M  = 22.08, SD = 1.185; 273 (54.4%) men, 229 (45.6%) women) in various fields of education (medical studies, sports, military and defence have not been covered). Fields of study have been registered for each bachelor student, and two categories have been constructed: STEM and non-STEM fields. The final sample included 250 STEM students and 252 non-STEM students. For university quality, performance scores of higher education institutions have been retrieved from the national university ranking for 2019 (Guo et al., 2023 ). Also, individual and background characteristics (academic performance, parents’ education, subjective family income, gender, age, area of residence, employment status, working experience), as well as subjective expectations regarding economic benefits anticipated upon the completion of a master’s degree have been collected from students.

The second level of the indicators includes data on the economic context at local level, more specifically on the business innovation environment. Regional-level data on enterprises introducing product and/or process innovations have been retrieved from statistical sources (Romanian National Institute for Statistics). List and description of indicators and data sources are presented in the appendix.

Aiming to identify the main determinants of students’ intention in pursuing a master’s degree in the next year, we used a multilevel logistic regression analysis based on the hierarchical nature of the data (individuals from different universities that are placed in different regions), which includes the individual-level variables, and then explores whether university-level factors together with regional level indicators are significantly associated with the intention of pursuing a master programme.

In order to analyse the between-region variation while taking into account the influence of individual characteristics in the overall intention to enrol to a master programme, different types of two-level models were used. The general approach of constructing the models is presented in Fig. 2 . The first stage was to estimate a baseline random intercept model with no explanatory variables. This step is needed for establishing whether a multi-level approach is appropriate. The null or empty two-level model with only an intercept and region effects has the following form:

This figure presents the step-by-step approach of the statistical analysis, outlining different types of the estimated two-level models.

The intercept \({\beta }_{0}\) is shared by all regions, while the random effect \({u}_{0j}\) is specific to region j . The random effect is assumed to follow a normal distribution with variance \({\sigma }_{{uo}}^{2}\) . The baseline random intercept model with no explanatory variables was estimated using maximum likelihood estimation using adaptive quadrature. The log odds are the logarithm of the odds (i.e. the ratio between a probability value (Phi) and its complementary).

The second stage was to develop a model with first-level variables (i.e. individual-level) in order to test the impact of individual characteristics:

β 0 is interpreted as the log-odds that y  = 1 when x  = 0 and u  = 0 and is referred to as the overall intercept in the linear relationship between the log-odds and x . If we take the exponential of β 0 , exp( β 0 ), we obtain the odds that y  = 1 for x  = 0 and u  = 0. As in the single-level model, β 1 is the effect of a 1-unit change in x on the log-odds that y  = 1, but it is now the effect of x after adjusting for (or holding constant) the group effect u . If we are holding u constant, then we are looking at the effect of x for individuals within the same group, so β 1 is usually referred to as a cluster-specific effect. Exp( β 1 ) can be interpreted as an odds ratio, comparing the odds that y  = 1 for two individuals (in the same group) with x -values spaced 1 unit apart.

While β 0 is the overall intercept in the linear relationship between the log-odds and x , the intercept for a given group j is ( β 1  +  u j ), which will be higher or lower than the overall intercept depending on whether u j is greater or less than zero. Therefore, u j is the group (random) effect, group residual, or level 2 residual. The response probabilities \({\pi }_{{ij}}\) can be expressed as follows:

At the second level, there will be added contextual factors to the model. In the third step, the logit random intercept model specification, including both individual-level explanatory variables, as well as region-level explanatory variables, is the following:

where: \({\beta }_{0}\) is the overall intercept, \({\beta }_{1}\) is the cluster-specific effect, \({\beta }_{2}\) is the contextual effect, X ij is the vector containing individual-level explanatory variables, X j is the vector containing region-level explanatory variables, and u j is the group (random) effect. The log odds are the logarithm of the odds (i.e. the ratio between a probability value (Phi) and its complementary).

Additionally, we test interaction effects exploring the possibility of the effect of one independent variable on the outcome to vary with the value of another explanatory variable. An interaction between a level 1 variable and a level 2 variable is called a ‘cross-level interaction’. Furthermore, it was worth to test if the effect of contextual regional factors on the decision of applying to a master programme depends on level 1 characteristics. Therefore, in the fourth step, we have estimated random intercept models with cross-level interactions.

In the random intercept models, the model intercept varies randomly across regions and the main assumption was that the coefficients of all explanatory variables are fixed across regions.

In the last stage, assuming that the decision of applying for a master's degree could vary across regions depending on the field of study—STEM (science, technology, engineering, math) and non-STEM, random slope models have been estimated, allowing for both the intercept and the coefficient of field of study to vary randomly across regions, also including cross-level interactions.

In a random slope model, a group-level random term u j has been included as a linear predictor of the model.

\({X}_{{ij}}\cdot {u}_{1j}\) is a new term to the model, 0 is the subscript for the intercept residual, random effects \({u}_{1j},\,{u}_{0j}\) are normally distributed with the variances \({\sigma }_{u1}^{2},\) \({\sigma }_{u0}^{2}\) and the covariance \({\sigma }_{u01}\) .

The extension from random intercepts to random slopes has introduced two new parameters to the model— \({\sigma }_{u1}^{2}\) and \({\sigma }_{u01}\) carrying out a test of the null hypothesis that both are equal to zero.

Also, regions showing an above-average positive relationship between the field of study and master enrolment intention will have \({u}_{1j}\, > \,0\) , while regions with a below-average positive (or possibly negative) relationship between the field of study and master enrolment intention will have \({u}_{1j}\, < \,0\) .

In order to test whether the effect of STEM compared with non-STEM fields varies across regions, a likelihood ratio test was applied, taking into account the difference in the log-likelihood values between the model with and without the random slope on STEM.

Empirical results and discussion

Results of the estimated models.

Concerning the predisposition phase in which students decide on pursuing a higher-level education programme, 53.6% of the total students report that they have the intention to enrol in a master's programme in the next year. Such intentions are more prevalent among STEM students (62%) than non-STEM students (45.2%). On the other hand, the Kruskal–Wallis test indicates statistically significant differences in this intention among students from different regions or universities. The empirical results indicate that students from the Bucharest region display the highest propensity to enrol in master's education.

The two-level model was used to allow for correlation between master enrolment intentions of individuals in the same region and to explore the extent of between-regions variation. The empirical results of the random intercept model with no explanatory variables revealed that the multilevel approach is suitable and estimated the log-odds of enrolment for an ‘average’ region to the value of 0.145. With a standard error of 0.086, we estimate the between-region variation of the log-odds of enrolment in a master programme at 0.094%. Also, the empirical results of the Wald test proved that there is a statistically significant variation between regions regarding the share of those applying for a master's programme. Based on the value of the between-regions variance (0.094), the variance partition coefficient (VPC) highlighted that 2.77% of the residual variation in the propensity of enrolment in a master programme is attributable to unobserved regional characteristics, indicating that almost 3% of the variance in applying to a master programme can be attributed to differences between regions. When exploring the university characteristics, the empirical results revealed the between-university variance of the log-odds of enrolment in a master programme estimated as 0.81 with a standard error of 0.50, and the Wald test pointed out a significant variation between universities in the proportion of those applying for a master programme. Based on the value of the between-university variance (0.81), the variance partition coefficient (VPC = 19.75%) indicated that almost 20% of the variance in applying to a master programme can be attributed to differences between universities. Thus, we have developed a two-step iterative procedure, building firstly a model only with individual level characteristics and then incorporating both level 1 and level 2 indicators, as well as interaction effects.

Table 2 reports the results of the random intercept models that only include the individual level variables , namely student-level predictors for model I, as well as university-level predictors for model II. The empirical results for the individual level variables pointed out the lack of significance for the association between gender, age, subjective income, expected full-time wage for a person who graduated a master degree or perceived share of unemployed with master’s degree. On the other hand, variables such as average grade, higher education of the father, seniority, or the type of working contract significantly influence the decision of applying to a master programme. Those with higher grades are significantly more inclined to apply for a master's degree, and so are students whose fathers graduated from higher education and students who work on a full-time contract. Additionally, students from rural areas and those with longer working experience have a lower propensity for enroling in master’s degrees. Perceived benefits associated with the graduation of a master's education in terms of wages and employment have no significant influence on the intention of enrolment.

Considering the variation induced by university-level factors, Model II shows that students enroled in non-STEM fields are less inclined to apply for a master's programme compared with those studying STEM disciplines, but the coefficient suffers from a lack of statistical significance at this point. Also, in the case of this model, the empirical results did not support the hypothesis that students decide to enroll in a master's program influenced by university performance.

Table 3 reports the results of the random intercept models that include the individual level variables (students and university characteristics) for model I and also region-level predictors for models II and III, together with the cross-level interaction terms. The findings of the random intercept model incorporating the individual level characteristics revealed that STEM students have a significantly higher predisposition to enrol to master education than non-STEM students. Also, the empirical findings indicate the positive influence of the university performance on the decision of students to enrol in master programmes. With respect to the individual factors introduced in the model, the results confirm that students with higher grades, those working full time and those whose fathers graduated higher education are more likely to apply for a master’s degree. On the other hand, students who have been employed for a longer period are less likely to pursue a graduate degree. The statistical significance of the above-mentioned individual variables preserved in all models. On the other hand, the intention to apply to a master’s programme remains unaffected by gender, age, residence area, subjective income, perceptions of the full-time wage of people with a master’s degree, or the percentage of unemployed people with a master’s degree.

Analysis of residual level-2 region effects (with only the individual characteristics-model I) supports the hypothesis that there are important differences among regions. North-West and Soth-West are the regions with the lowest probability of applying to a master programme (largest negative values of uj ) for which the confidence intervals do not overlap with 0, indicating that they have a significantly lower probability of enrolment than the average region. At the upper end, Bucharest-Ilfov and Centre are the regions with intervals that do not overlap with 0 with the highest response probability (largest positive values of uj), indicating a significantly higher probability of applying to a master programme compared with the region average.

Considering the regional context, model II indicates that the percentage of businesses that introduce product innovations has a detrimental effect on the choice to apply to a master programme. It appears that a more innovative corporate environment might provide alternative incentives for students making them less likely to enrol in master’s programmes.

In addition, model III analyses cross-level interactions between the field of study and the intensity of business innovation. Results show that STEM students are more impacted by the share of enterprises introducing product innovations than non-STEM students when deciding whether or not to apply to graduate school. On the other hand, the percentage of enterprises that introduce process innovations and enterprises that introduce product and process innovations exhibit no influence on the intention to pursue a master’s program.

Until now, we have found that the intention of enrolment in a master programme depends on several student, university, and regional innovation characteristics and this was achieved by allowing the models intercept to vary randomly across communities in random intercept models. We have assumed that the effects of individual characteristics are the same in each region, i.e. the coefficients of explanatory variables are fixed across regions.

In this stage, we extend the random intercept model, allowing both the intercept and the coefficient of one of the explanatory variables to vary randomly across regions, making the assumption that the probability to apply to a master programme could vary from region to region depending on STEM vs. non-STEM majors. Random slope models with both individual variables and regional level variables together with cross-level interaction have been estimated and the empirical results are presented in Table 4 .

Thus, the model of master’s programme enrolment intentions was updated to compensate for variances in STEM vs. non-STEM disparities among regions. It is assumed that the only variation in the association between the field of study and region is in the difference between STEM and non-STEM in this model, which allows for various probabilities of master enrolment for different fields of study (as in the random intercept model above). It is calculated for Model I with just level 1 individual variables that the coefficient of the field of education (the difference between STEM and non-STEM) is 0.835 +  u 1 j in the corresponding region j . STEM field has a random coefficient, which suggests that the variance between regions relies on the field of education (STEM vs. non-STEM). Master programme enrolment chances differ between STEM and non-STEM fields by 0.062 and 1.29, with the intercept variance interpreted as the between-region variation in log-odds, respectively. As a result of the negative intercept-slope covariance estimate, it may be concluded that regions with an above-average likelihood of master’s degree enrolment (intercept residual u 1 j  > 0, slope residual u 1 j < 0) are likely to have lower-than-average impact on STEM field. Based on the LR test, where the null hypothesis of no region variation in the difference between STEM and non-STEM students was tested, we may infer that the gap between the two educational fields does indeed change between regions. The difference between communities is now calculated as follows:

=0.062–0.215 \({x}_{{ij}}\)  + 1.29 \({x}_{{ij}}^{2}\) which because STEM students ( x ij ) can only take values of 0 and 1, simplifies to: 0.062 for STEM = 0 and 1.137 for STEM = 1. Therefore, between-region differences in the intention of applying to a masters' programme are greater for STEM students, while regional variation for non-STEM students is lower.

As in the case of random intercept models, STEM students have higher propensity for enroling in masters' education. Also, the intention of applying to a master's program is influenced by academic performance, the higher education of the father, work seniority, and a full-time working contract (Model I), and the significance of these variables is preserved in all models.

Adding and testing the region variables show that the share of enterprises introducing product innovations negatively impacts the decision of applying to a master programme (Model II). Thus, regions with higher shares of innovative enterprises are characterised by a lower propensity of students to enrol in masters' education. A more innovative business sector discourages Romanian students to prolong their educational careers, offering attractive incentives to enter labour market after bachelor’s degree.

Moreover, the effect of the proportion of enterprises introducing product innovations affects the decision of applying for a masters' programme more effectively for STEM students compared with non-STEM students (Model II). For an increase in the proportion of enterprises with product innovation, the effect positively depends on the field of education and the effect will be higher for STEM students. So, the influence of a more innovative business context is higher among STEM students than non-STEM students. This cross-level interaction between the major of the students and the innovation intensity from the regional level indicates why and how the differences between STEM and non-STEM students in terms of master enrolment are not similar across regions.

Discussion of the results

Choice of enrolment in master education.

This study supports recent educational choice models that include along with the insights from the human capital theory, cultural and social capital embedded in individual characteristics and background (Perna, 2006 ; English and Umbach, 2016 ). Thus, our results confirm the influence of various individual-level factors shaping the decision of prolonging the educational career. Similar to the findings of other scholars (Latiesa, 1989 ; Mullen et al., 2003 ; English and Umbach, 2016 ; Zamfir et al., 2021 ), we found that students with higher academic performances are more interested to continue their education with a master degree, suggesting that the grades’ level influences perceived academic self-efficacy and expectations regarding future academic success, encouraging or discouraging students to continue their education. With respect to cultural capital, our results point out that higher education attainment of the father is associated with a higher propensity of enrolment in a master's programme. This is according to the theory of Bourdieu ( 1977 ) linking educational success to the possession of embodied cultural capital, which determines cultural and social reproduction across generations. With respect to institutional factors, we found that the performance of universities influences the intentions of students to pursue master programmes. It seems that students in universities with higher performance are more satisfied and more inclined to enrol in master programmes.

Furthermore, our results provide evidence supporting the influence of predictors derived from the human capital theory, with some particular aspects that appear in relation to master education. First, our expectations concerning the influence of traditional predictors, such as the perceived benefits upon completion of the education programme were not confirmed. Instead, variables related to the individual demand for specialised skills were found to significantly influence the predisposition of bachelor students to enrol in master education. We found that students working full-time are more inclined to apply for a master's programme than those not working. As attending master programmes is a way of acquiring specialised skills, those working are those who can use such skills and benefit from them. The demand for specialised skills is higher among those who work full time, suggesting that students who work expect higher returns from pursuing a master’s degree than those not working. The latter are less likely to take the risk of accumulating more education for future gains than those who are full-time employed. Master programmes are seen to bring higher returns for insiders on the labour market, rather than for outsiders. This is consistent with the theory that “insiders” often enjoy better employment opportunities than the “outsiders” (Lindbeck and Snower, 2001 ), allowing them to benefit more from continuing their education at master-level. On the other hand, seniority is associated with a decrease in the predisposition of applying to a master programme. As individuals accumulate experience in the workplace, they are no longer in demand for acquiring specialised skills. So, we can consider that master programmes are seen as providing specialised skills required on the labour market, necessary for those who didn’t acquire such skills by longer working experience.

Regional variation in the influence of STEM and non-STEM majors on the choice of enrolment in master education

First, the current study confirms that predisposition for pursuing master’s degrees is influenced by the field of education. We found that, generally, STEM students are more interested in master’s degrees than non-STEM students. It suggests that both the demand for highly specialised skills and advantages obtained by master graduates vary in relation to STEM vs. non-STEM fields (Lee et al., 2020 ).

On the other hand, we find evidence indicating significant regional variability in the intentions of enroling in master programmes. This is in line with the idea that returns to education vary across regions within a country (Backman, 2013 ) as the gains from education are determined by the local labour market (Combes et al., 2008 ). According to the new economic geography theory (Krugman, 1991 ), core regions provide higher returns than other regions. Our results confirm this perspective and show that intentions of enrolment in master education are higher in large regions with intensive economic activity, such as the Bucharest-Ilfov region, which includes the capital of the country.

More importantly, our results show that the local context is not independent of the field of education when shaping students’ educational choices. There is a significant regional variation in the difference between STEM and non-STEM students. According to our findings, between-region differences in the intention of applying to a master programme vary in relation with the field of education, showing the complex way in which local conditions interplay with graduating STEM vs. non-STEM fields. So, differences between the propensity of STEM and non-STEM students to enrol in master education varies across local contexts.

More exactly, between-region differences in the intention of applying to a master's programme are greater for STEM students than for non-STEM students. This is consistent with the concept of constrained choice, reflecting the way structural factors interplay with individual decision-making and influence the educational pathways of students (Kurlaender and Hibel, 2018 ). So, our results suggest that decisions of STEM students in relation to master education are more sensible to the local context factors, while choices of non-STEM students are more invariant across regions. As a result, the difference between the intentions of STEM and non-STEM students varies in relation to the local context, further confirming the theory that core regions provide higher returns to the accumulation of master education than other regions.

The role of the business innovation context in shaping predisposition for master education of STEM and non-STEM students

The empirical results confirmed the influence of local-level factors and showed that the intensity of the business innovation is of relevance. We found evidence that more innovative business contexts are less conducive to the enrolment of students in master programmes. Our results suggest that students living in regions with more developed business innovation display a lower propensity to pursue master programmes, probably due to higher foregone earnings. From this point of view, more innovative economic environments act as a pull mechanism for bachelor graduates, preventing them to prolong their educational careers.

Moreover, innovative business environments discourage more STEM students from further continuing their education. These students are probably those who expect higher immediate earnings in enterprises introducing product innovations, preventing them from further enrol in master education. On the other hand, non-STEM students are less influenced by the regional innovation conditions and their intentions to pursue master education are more invariant across regions. Our findings suggest that forgone and future earnings of non-STEM students are more similar across regions with different levels of business innovation intensity. This suggests that immediate earnings available to non-STEM graduates are less fuelled by the local innovation ecosystem.

So, in general, after controlling for various individual and institutional factors, STEM students are more interested in pursuing master's education than non-STEM students, suggesting that they anticipate higher returns when accumulating master-level education. However, STEM students’ intentions are more influenced by the regional innovation conditions as they are more discouraged to enrol in master education than other students by more innovative business contexts. It seems that such contexts provide immediate attractive incentives for STEM bachelor graduates, preventing them from further investing in master's education. Individuals decide to further accumulate human capital as long as they anticipate that future additional earnings are higher than the direct and indirect costs of continuing education. From this point of view, our results suggest that STEM bachelor graduates in innovative business contexts are discouraged from continuing formal education due to higher immediate earnings. On the other hand, enterprises with a higher propensity to innovation are also the ones providing more employer-funded training (Toner et al., 2004 ). This could represent an alternative way to acquire specialised skills, replacing the demand for master's education.

The proposed model should be further tested on more comprehensive sets of data, covering more variate educational and economic contexts. Regarding the relevance of our findings to other contexts, one has to consider the economic, cultural, and educational diversity of different regions. For instance, countries with more developed innovative ecosystems and strong links between education and industry may display a more positive influence of business innovation on participation in master education. On the other hand, the influence could differ in regions where the innovation ecosystem is developing or educational systems are less connected with the industry. Acknowledging the study’s geographic limitation is important. While the results provide valuable insights into the relationship between master education and regional innovation conditions in Romania, these findings may not be relevant to other contexts without considering differences in education and economic systems. This limitation points to the need for localised studies or comparative research addressing similar research questions. Future research that can address such limitations and explore the model’s relevance in varied contexts can include cross-country comparative studies.

Final reflections and policy implications

Educational decisions are an important topic of study for education research. The results of this study are in line with recent educational choice models that include individual, institutional and economic characteristics among factors shaping decisions of enrolment in master education (Perna, 2006 ; English and Umbach, 2016 ). Consistent with Bourdieu's ( 1977 ) theory, we found that parent education and academic performances predict the propensity towards master education, confirming the results of previous studies (González and Dávila, 1998 ; Latiesa, 1989 ; Jiménez and Salas-Velasco, 2000 ; Mullen et al., 2003 ; Perna, 2004 ; Xu, 2014 ). Also, according to our results, university performances shape the intentions of students to pursue master’s degrees, supporting conclusions of other studies (Schapiro et al., 1991 ; Hartman and Schmidt, 1995 ; Kallio, 1995 ; Zhang, 2005 ). In addition, we provide evidence that the expected benefits of a master's education are higher among full-time workers, especially at the beginning of their careers.

On the other hand, our study complements the literature in the field of master’s degree attainment in STEM education. With respect to differences across fields of study, we show that STEM students are more interested in master’s degrees than non-STEM students. This research covers a knowledge gap related to the extent to which differences between STEM and non-STEM majors are influenced by local contexts and economic conditions. More exactly, the proposed model and our empirical results provide a better understanding of the local context's influence on the educational choices of STEM and non-STEM bachelor students. By employing a multi-level model, we confirm that educational choices are shaped by a dense combination of factors, including individual, university and local level factors. Moreover, we show that the influence of local circumstances depends on the individual-level factors. Local conditions regarding business innovation influence educational choices differently in relation to the field of education. STEM students’ propensity to enrol in master’s degrees is more influenced by the innovation environment than other students.

Understanding how individual, institutional, and contextual factors influence the intention to pursue master’s degrees can be beneficial for improving STEM master’s level programmes efficacy. Our study allows the formulation of several recommendations and implications for STEM higher education policy and practice, including for the widening participation agenda.

First, our study confirms that, in general, the propensity to a linear transition from bachelor to master’s degrees is higher for specific groups of students, such as students with better academic performances, those from families with higher educational attainment, students working full time and those at the beginning of their working history. From this perspective, universities need to find mechanisms for enhancing the access of students with lower grades or from less educated families to master-level education and to adapt the way such programmes are delivered to the needs of working students in early career stages. In particular, financial support schemes could be beneficial for students interested to pursue master's education but are discouraged by the opportunity cost of not entering the labour market immediately. Such support schemes would include scholarships and special loan conditions available for students from less advantaged backgrounds. Moreover, master programmes are expected to provide specialised skills that are used in the workplace. Thus, universities need to enhance their link with the world of work and design master programmes that are closer to the skills demands of the companies. Educational institutions should revise curricula to increase their relevance and strengthen partnerships with the industry to provide flexible, relevant learning opportunities and practical work experience for students, matching education with the demands of business environments. For instance, collaborative programmes between businesses and universities could provide opportunities for students to acquire practical experience through work-based learning while still enabling students to pursue master-level education. Such an approach combines the benefits of immediate job placement with ongoing education. Also, by improving the overall quality of their educational process, universities can expect to retain more bachelor graduates in their master's programmes.

Second, our results show that STEM bachelor graduates anticipate unattractive net benefits from pursuing master's education in more innovative business contexts, probably due to higher forgone earnings. This conclusion is consistent with the idea that highly innovative local contexts attract highly skilled people and talents to a greater extent (Toner, 2011 ). In such dynamic innovation landscapes, STEM educational institutions need to strengthen their synergy with the local business sector and improve opportunities for master students to work while studying. Thus, providing incentives and developing collaborative structures between universities and the business environment could balance immediate job opportunities with increased long-term returns of continued education for STEM graduates in innovative regional contexts. For instance, tax incentives granted to enterprises that stimulate employees to pursue master's education through funding or paid leave could help mitigate the trade-off STEM graduates face between full-time immediate employment in innovative enterprises and continuing education. In addition, more flexible learning pathways would allow STEM students to engage with the industry while pursuing master's studies. This could include part-time study options, industry placements as part of the learning programmes, or projects in collaboration with local businesses. In particular, expanding dual education within STEM master programmes would be beneficial for retaining bachelor graduates and improving the capacity of STEM education to respond to local skills demand. In addition, career guidance services should be enhanced to better inform undergraduate students about the long-term returns of master's education versus the immediate benefits of entering employment. This guidance should be tailored to the specific context of the students’ major and regional innovation environment.

We conclude that human capital theory continues to provide a valuable framework for understanding educational choices, especially in the case of STEM fields. Future research will focus on longitudinal studies to track STEM and non-STEM graduates’ long-term career outcomes. This would offer insights into the relevance of masters' education for the skills demands of local industries. Comparative studies across different regional innovation ecosystems can also shed light on how specific local conditions allow graduates of various fields of education to benefit from pursuing masters' education. From the methodological perspective, this study highlights the importance of robust approaches to understand the complex dynamics between education, career choices and local economic contexts. Future research should consider mixed methods designs that combine quantitative analysis with qualitative insights from students, educational institutions and industry stakeholders. This would offer an in-depth understanding of graduates’ motivations, barriers, and opportunities.

Data availability

The survey dataset analysed during the current study is available as a supplementary file.

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Acknowledgements

This research was funded by the Ministry of Research, Innovation, and Digitalisation of Romania under NUCLEU Programme PN 22100102. The funding body was not involved in the design of the study, data collection, analysis, interpretation, or writing of the manuscript.

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Zamfir, AM., Davidescu, A.A. & Mocanu, C. Understanding the influence of business innovation context on intentions of enrolment in master education of STEM students: a multi-level choice model. Humanit Soc Sci Commun 11 , 1087 (2024). https://doi.org/10.1057/s41599-024-03601-5

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Rita S. Senise

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Senise, R.S., Yogui, R., Cirne, L.F. (2021). Role of Science, Technology, and Innovation Towards SDGS. In: Leal Filho, W., Marisa Azul, A., Brandli, L., Lange Salvia, A., Wall, T. (eds) Partnerships for the Goals. Encyclopedia of the UN Sustainable Development Goals. Springer, Cham. https://doi.org/10.1007/978-3-319-95963-4_90

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