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INVESTIGACIÓN, DESARROLLO
E INNOVACIÓN
A framework for the institutional
analysis of research organizations
INTA
Javier Ekboir
Serie: Documentos de trabajo del CICPES. Nº 10/2016
Javier Ekboir
A framework for the institutional analysis
of research organizations INTA
Centro de Investigación en Ciencias Políticas,
Económicas y Sociales
2016
A framework for the institutional
analysis of research organizations
INTA
Investigadores:
Javier Ekboir1
INSTITUTO NACIONAL DE TECNOLOGÍA AGROPECUARIA
Centro de Investigación en Ciencias Políticas, Económicas y Sociales
Tel: 4338-4600
Av. Rivadavia 1439 (C1033AAE)
C.A.B.A. - Argentina
2016
Coordinador Editorial: Gabriel Delgado
Editor: Ana Laura Schonholz
Diseño de tapa e interior: Mariano Mancini
©, 2016, Ediciones INTA
Esta publicación es propiedad del Instituto Nacional de Tecnología
Agropecuaria - INTA
Domicilio Legal Rivadavia 1439, cp. 1033, CABA
Propiedad Intelectual: En trámite
Serie: Documentos de trabajo del CICPES. Nº 10/2016
1
INTA - CICPES
Como citar este documento: Ekboir, J. (2016). "A framework for the institutional analysis
of research organizations INTA". Serie: Documentos de trabajo del CICPES. Nº 10/2016.
INTA.
Ciudad Autónoma de Buenos Aires: Ediciones INTA. ISSN 1514-0555.
Executive Summary
The environment in which public research organizations operate is changing at an increasing
pace, creating the demand that they to adapt. However, these organizations are structured to
operate in relatively steady environments and they do not have the tools to function in
situations that are in constant flux. This paper presents a framework for conducting an
institutional analysis of public research organizations; the framework is based on novel
science and innovation policies and the trends that have recently been documented in the
organization and management of science. A second document, prepared within this project,
contains the results of a study that used this framework to analyze Argentina’s National
Agricultural Technology Institute (INTA).
a) The framework for the institutional analysis of public research organizations
The framework for the institutional analysis of public research organizations characterizes
them as complex organisms that evolve in response to external and internal factors. These
factors can be grouped into motivations and capacities. The main motivations for public
research organizations are the desire to contribute to social wellbeing and the advancement
of science, external pressures (from both the policy makers and budget allocations), and
scientific progress. Capacities determine how organizations respond to new challenges or
opportunities. The capacities are built through sustained investments and depend on several
factors, including the history of the organization, its culture, governance, the presence of
innovative individuals and the environment in which it operates. The influence of each
factor changes over time, so that variables that have a positive effect at a given moment can
become negative factors later.
The evolution of organizations is also influenced by the mechanisms they use to define their
strategies and to implement them. All organizations have two simultaneous strategies:
deliberate and emerging. Deliberate strategies are those formally defined by the
organization while emerging strategies result from the operational decisions of each
member of the organization. The accumulation of these operational decisions result in the
actual allocation of resources, which may differ from the allocation decided in the deliberate
strategy. Operational decisions tend to be tactical and include, for example, the allocation
of resources within programs or deciding which farmers to visit. Without effective
leadership and learning mechanisms, emerging strategies dominate the evolution of
organizations, preventing them from adapting to emerging opportunities or problems.
Organizational capacities are built through sustained investments, strong leadership and
careful selection of those responsible for carrying out the capacity development activities.
It is also essential that the senior management (a) help to install a shared vision of the
changes that will be implemented, (b) allow an adequate number of pilot projects to reduce
uncertainty and create mechanisms for effective feedback and (c) promote discussions
internal to the organization and with stakeholders to agree on what is desirable and
acceptable by the organization.
Three of the most important characteristics that influence organizational innovation
capacities are the learning mechanisms (because they determine how quickly new
information can be generated and used to improve the organization’s activities), the
organizational culture (because it determines how “unconscious” factors influence
organizational change) and the governance arrangements (because they define how flexible
the organization is when organizing its activities and resolving conflicts). Typically it is
difficult for organizations to learn because (a) they tend to prefer routines that have so far
been successful to new approaches that could be more effective; (b) in order to obtain results
consistently they cannot permanently change what they do and how they do it; and (c)
directors, managers and employees tend to be absorbed by routine activities. Organizational
learning includes the processes by which knowledge is created and distributed in the
organization and is integrated into its operations. Organizational learning is a lengthy
process that only happens when it is strongly promoted by the organization’s decision
makers and when appropriate incentives are introduced.
The organizational culture is a set of assumptions, values and beliefs developed by an
organization. Some cultures facilitate individual learning while others encourage collective
learning; similarly, some cultures emphasize centralized and vertical management, while
others promote horizontal decision making. No culture is superior to others in all situations;
moreover, some cultures may be appropriate in certain contexts and become a disability
when the organization or the context changes.
Organizational governance mechanisms include three dimensions: structure (distribution of
functions and coordination); processes (communication, coordination, leadership, learning
mechanisms, operational processes and incentives); and the strategic priorities (mission,
vision, strategies and action plans). The structure defines the formal authority, functions,
responsibilities and lines of communication between the parts of the organization; processes
are recurring activities that use the organization’s resources and strategies define the
objectives and action plans. In non-profit organizations, strategic axes are essential to align
the actions of all members. While organizational cultures change slowly and are largely an
emergent property of the evolution of the organization, governance mechanisms can change
very quickly by deliberate actions.
An important element in the institutional analysis of research organizations is the set of
incentives they offer to their scientists because they align individual actions with
organizational objectives. It is important to consider the whole set of incentives, both
monetary and non-monetary, such as professional development opportunities, as it has been
determined that researchers strongly value the possibility of being the first to answer
“interesting” questions and be recognized for it. This does not mean that monetary
incentives are not important, but they are not the only issue to be considered. The
importance of non-monetary incentives is exemplified by the rapid expansion of open-
source software production (Friedman 2016).
b) Changes in the organization of science
The organization of science is being transformed by rapid scientific and technological
progress as well as environmental, economic and social changes (e.g., climate change,
globalization, migration and demographic and nutritional transitions). The most important
changes include:
• An increasing share of research is done outside traditional research centers such as US and
European universities and institutes.
• Disciplines are evolving as new disciplines emerge at their borders and transdisciplinary
approaches become more common.
• Researchers cannot follow all developments in their own discipline and area of work,
forcing them to work increasingly in transdisciplinary networks.
• More countries are doing research but it is still heavily concentrated in developed countries
and China.
• In the stronger research systems the number of tenured positions is shrinking leading to
greater employment and financial insecurity.
• Public financing of research is shifting from blind grants to time-bound projects, and an
increase in research funded by private companies and billionaires.
• Technical change in science is accelerating the obsolescence of equipment and
professionals.
• Increasingly research processes and inputs are protected by patents and other intellectual
property rights; therefore, public research organizations need to develop strong capacities
in these areas to protect their research outputs and to analyze the implications of
collaborating with other actors in research and innovation networks.
Contents
1 Introduction................................................................................................................1
2 Conceptual framework for analyzing public research organizations.........................2
3 How is agriculture changing? ....................................................................................5
4 What is an innovation?...............................................................................................7
5 Some features of complex systems important for the analysis of research and
innovation............................................................................................................................10
6 Characteristics of research activities........................................................................12
7 The relationship between scientific research and innovation ..................................14
8 Trends in the organization of science ......................................................................16
8.1 How the rapid advance of science affects research organizations...........................16
8.2 New areas of research have emerged, but most resources are invested in a few areas
(TICs and life sciences, especially related to human health)...................................17
8.3 Increasing privatization of the cutting-edge research in some areas .......................18
8.4 Public research is more distributed globally but it is concentrated in a few countries;
additionally, private research in developed countries is increasingly concentrated in
a few companies.......................................................................................................20
8.5 The number of tenured research positions in both the USA and Europe is falling
while the number of Ph.Ds. is growing....................................................................20
8.6 Science is increasingly conducted by international networks..................................21
8.7 Financing of time-bound projects is increasing relative to blind grants..................21
9 Changes in extension systems..................................................................................21
10 Framework for the organizational analysis of research organizations.....................24
10.1Organizational capacities for innovation .................................................................26
10.2Organizational learning............................................................................................28
10.3Organizational culture..............................................................................................30
10.4Organizational systems of government....................................................................32
10.5Incentives offered to researchers, including remuneration, working conditions and
financing mechanisms..............................................................................................33
11 New approaches for the management of public research ........................................35
11.1Adaptive management .............................................................................................36
11.2The importance of a good monitoring and evaluation system (M&E) ....................37
12 Final Considerations ................................................................................................38
13 Bibliography ............................................................................................................40
1
1 Introduction
The socioeconomic environment in which public agricultural research organizations operate
is changing at an increasing pace, both in Argentina and the world.1
These changes and new
social visions on the role of science in development are imposing new demands on public
research organizations. Responding to these demands, Argentina’s Instituto Nacional de
Tecnología Agropecuaria (INTA) commissioned the analysis of the most important trends
that are currently influencing the organization of research globally and, in particular, in
middle income countries. This paper contributes to the debate about the emerging
opportunities and challenges faced by these organizations, especially INTA, by presenting
a framework for:
• Analyzing the most important recent trends that have been documented in the organization
of science, especially in developed countries.
• Conducting an institutional analysis of public research organizations.
• Reviewing new guidelines for the design and implementation of science and innovation
policies.
Scientific and technical progress and environmental and socioeconomic changes are
changing societies and nature faster and in unpredictable ways, forcing organizations to
change more frequently (Friedman 2016; McChrystal et al. 2015). But most organizations,
both public and private, have serious difficulties adapting (Christensen 2003). To overcome
them, they need to develop change strategies and monitor their implementation. The
framework presented in this document is based on the characterization of public research
organizations as complex entities that evolve in response to external and internal factors.
The evolution depends, among other factors, on their organizational capacities and cultures,
learning mechanisms (i.e., the creation of new capacities) and governance structure.
Public research organizations can be characterized as complex organisms that evolve
through the interaction between motivations and capacities. Motivations reflect the
opportunities for change and in the case of public research organizations, they include
external pressure (reflected in the political discourse and budgetary allocations), scientific
progress, and the desire to contribute to social welfare and the advancement of science.
Capacities determine how organizations respond to challenges or emerging opportunities.
The capacities are built through sustained investments and depend on a number of factors,
including the history of the organization, its culture, governance mechanisms, the presence
of innovative individuals and the environment in which the organization operates. The
influence of each factor changes over time, so that the variables that at a given moment have
a positive effect could become negative factors later.
A serious handicap of public organizations is the lack of a clear metric to follow whether
they are achieving their strategic objectives. In private companies the actual or expected
profits serve this purpose. But not-for-profit research organizations that seek to contribute
1
In this paper agricultural research includes all research related to productive activities in rural areas, including
forestry, natural resources management and genetic resources.
2
to socioeconomic wellbeing and the sustainable use of natural resources lack simple and
relatively easy to measure indicators. These organizations can overcome this problem by
(a) clearly defining their strategies to guide their decisions and those of their members, and
(b) introducing flexible incentives, processes and effective mechanisms for organizational
learning (Hudson 2009). Setting organizational strategies, developing capacities and
implementing organizational learning are discussed in detail in sections 2, 0 and 11.
The most important external changes that are influencing research organizations can be
classified into three categories:
• Changes in the agricultural sector and in the management of natural resources (both
at the national and global levels), including new techniques and science-intensive
products and ICTs.
• Changes in the social perception of science.
• Changes in the organization of science.
The changes in the agricultural sector arise mostly from the integration of output, financial
and labor markets (in which migration has a strong impact on both the countries of origin
and destination), changes in economic policies, technological change and scientific
advances. Section 3 reviews these changes.
In 1945, Vannevar Bush, then director of the U.S. Office of Scientific Research and
Development published a paper that marked science policy for nearly four decades (Bush
1945). This paper introduced what became known as the linear vision of science, and had
three basic principles: (a) basic and theoretical research were the basis of all scientific and
technological progress, (b) after a while basic research always benefits society, and (c)
science can only be evaluated by scientists. That is, society should finance science
abundantly and delegate to the scientific community its own regulation (Kraemer 2006).
This view began to change in the 1980s when it seemed that Japan, with a research system
much smaller than that of the U.S., grew faster and appeared to be on track to become the
largest economy in the world. The analysis of the contrasting experiences of the U.S. and
Japan originated the studies on innovation systems (Freeman and Soete 1997), discussed in
sections 4, 5 and 7. New models for organizing and controlling science emerged from these
studies (Stephan 2012). Currently these trends are accelerating by rapid scientific advances,
new mechanisms of interaction between different actors in the innovation systems and new
science and technology policies. These changes affect all organizations involved in science,
including public and private research organizations, universities, governments, businesses,
the researchers themselves and extension organizations; these issues are discussed in
sections 8 and 9. Finally, sections 0 and 11 discuss important concepts for the institutional
analysis of public research organizations.
2 Conceptual framework for analyzing public research organizations
The conceptual framework for the institutional analysis of public research organizations
recognizes these organizations as complex organisms that evolve by interaction between
motivation and capacities (Ekboir et al. 2009). The motivation reflects the incentives and
opportunities to change and include external pressure (from policy makers and budget
3
allocations), science progress, and the desire to contribute to social welfare and the
advancement of science. In contrast, for private companies motivations are mostly market
or technical opportunities. Finally, for civil society organizations, the greatest motivation is
their commitment to a social or environmental cause.
Capacities determine how organizations respond to challenges or emerging opportunities.
The capacities are built through sustained investments and depend on several factors,
including the history of the organization, its culture, governance arrangements, the presence
of innovative individuals and the environment in which the organization operates. The
influence of each factor on the dynamics of the organization changes over time, so that
variables that have a positive effect at a given moment can become negative factors later
(Christensen and Raynor 2003).
The dynamics of organizational capacities and their interaction with external factors can be
explained by the properties of Complex Adaptive Systems, known as CAS. These systems
evolve by the interaction among large numbers of actors of different types, conditioned by
the history of the process, the socioeconomic environment in which they operate and
random events (Crutchfield and Schuster 2003). In the case of public research organizations,
the internal actors are mainly researchers and managers; external stakeholders include
policy makers, producers, producer organizations, private companies and financial
institutions.
Organizations implement strategies through two simultaneous processes:
 Deliberate strategies are those formally defined by the organization usually through
strategic planning exercises.
 Emerging strategies are those that result from the accumulation of the decisions of each
and all members of the organization, including top and middle managers, sales force,
researchers and floor operators. These decisions tend to be tactical and include, for
example, the allocation of resources within the divisions or deciding which partners to
prioritize.
The accumulation of the operational decisions results in the actual allocation of resources
within the organization, which may differ from the allocation decided in the deliberate
strategy (Mintzberg and Waters 1985). Without effective leadership and learning
mechanisms, emerging strategies dominate the evolution of organizations and organizations
only react to emerging problems or opportunities.
Three of the most important features that influence organizational innovation capacities are
the learning mechanisms (because they determine how quickly new information can be
generated and used), the organizational culture (because it determines how “unconscious”
factors influence innovation activities) and the governance arrangements (because they
define the flexibility of the organization to organize their activities and resolve conflicts).
Innovation processes are complex because if they could be planned in detail they would be
routines, not innovations. The ability of innovators to try and find new things that work
better than those in use depends on their ability to integrate into networks that facilitate the
sharing of resources and information. In other words, innovations depend on the interaction
4
between individual skills and organizational learning (Dosi, Nelson and Winter 2000).
Individual capacities result from the combination of a person’s natural talent and learned
skills (Renzulli 2003). On the other hand, organizational capacities result from the
interaction between the resources available in the organization (skills of individuals and
fixed capital), processes (mechanisms accepted by the organization as the way to do things)
and values (including the institutional culture and the long-term goals).
Organizational capacities can be built through sustained investments, strong leadership and
a proper selection of those responsible for carrying out the activities. It is also essential that
the senior management actively foster organizational learning, i.e. (a) help to develop in the
entire organization a vision of the changes that must be made, (b) in order to reduce
uncertainty, allow an adequate number of pilot projects to identify appropriate changes, and
(c) install effective discussions and feedback mechanisms to reach consensus on what is
desirable and acceptable in the organization (Davila, Epstein and Shelton 2006; Crutchfield
and Schuster 2003; Levinthal 2000). The effectiveness of the feedback mechanisms is
essential because the number of options that an organization can try is limited by problems
of coordination, availability of resources and organizational skills (Dosi, Nelson and Winter
2000).
Learning is also essential in order to take advantage of emerging strategies. However
organizations typically have difficulties to learn and innovate because (a) they tend to prefer
routines that have so far been successful to innovative approaches that could be positive,
and (b) the directors, managers and employees tend to be absorbed by routine activities
(Christensen and Raynor 2003; Dosi, Nelson and Winter 2000).
Organizational learning begins when the individuals in the organization can better process
information to build new frameworks. Individual learning is also a collective process
because what an individual learns depends to a large extent on what the other members of
the organization know and what the organization allows in terms of experimentation (Bailey
and Ford 2003). The organizational learning includes the processes by which knowledge is
created and distributed in the organization and is integrated into its operations (Dosi, Nelson
and Winter 2000).
The organizational culture is a set of assumptions, values and beliefs developed by an
organization, mostly in its beginnings and molded by practice over the years. The
organizational culture determines to a large extent what is acceptable in the organization
(Schein 1991). Some cultures facilitate individual learning while others promote collective
learning; similarly, some cultures emphasize centralized and vertical management, while
others promote horizontal decision-making mechanisms. No culture is superior to the others
in all conditions; even more, some cultures may be appropriate in certain contexts and
become a hindrance when the organization changes.
The analysis of governance in the organization includes three dimensions: structure
(distribution of functions and coordination); processes (communication, coordination,
leadership, learning policies and operational processes) and the strategic axis (mission,
vision, strategic lines and action plans). The structure defines the formal lines of authority,
roles, responsibilities and lines of communication between the parties of the organization;
5
processes are recurring activities that use the resources of the organization and the strategic
axis define the objectives and plans of action. In non-profit organizations, the strategic axes
are essential to align the actions of all members. While the organizational cultures change
slowly and are mostly an emerging property of the organization’s evolution, governance
arrangements can change very quickly by deliberate actions (Mintzberg 1999).
Individual actors and organizations do not possess all the resources they need to innovate;
therefore, they integrate into networks that facilitate the exchange of knowledge, capacities
and resources (Powell and Grodal 2005). Networks are organizations with informal
structures and a relatively fluid membership.
3 How is agriculture changing?
Several publications analyze current changes in the production, marketing and consumption
of agricultural products; therefore, this document only discusses the influence of these
changes on the organization of agricultural research and extension. Additionally, due to the
nature of the study and to the fact that it was commissioned by an Argentine organization,
it does not discuss in sufficient detail the situation of the rural poor and small producers in
developing countries.
Markets for agricultural inputs and outputs are increasingly integrated globally, natural
resources are changing (especially climate change and water use) and technical change is
accelerating; these trends have strong impacts on farmers. For commercial producers,
factors that are not strictly agronomic (such as managerial and commercial issues) are
becoming progressively important, forcing them to develop new capacities (this topic is
discussed in greater detail in section 10.1). On the other hand, more off-farm employment
opportunities, either in urban areas or abroad are opening for households that derive only a
relatively small share of their income from agriculture. As the importance of agriculture as
a source of income shrinks, these families devote fewer resources to it and prefer to invest
in health and education which allows them to better access more profitable markets (Ekboir
2016).
Globally farms are converging towards five major production schemes: 2
a) Large-scale production of grains and meats.
b) Plantations that produce fresh fruits or inputs for industry such as palm oil, coffee or
tea.
c) Production of fresh fruits and vegetables which in turn can be differentiated among those
who sell to supermarkets and fast food chains on the one hand and those who sell
through traditional channels, such as at the farm-gate or in urban wholesale markets, on
the other.
2
While these categories can be found all over the world, their relative importance and characteristics vary
across countries. For example, small farmers who produce high-value products in the east coast of the U.S.
are very different from those of Mexico. Similarly, small-scale traditional producers in Brazil relate differently
with large commercial farms than those in Burundi.
6
d) Farmers who sell directly to consumers (for example, in farmers' markets or home
delivery).
e) Poor rural households that earn a significant portion of their income from off-farm work
and have a diversified agricultural production, mainly for home consumption, and
eventually sell small surpluses.
The first three categories operate increasingly in vertically integrated chains, in which the
producers who do not invest in production, management and marketing technologies
disappear due to the strong economies of scale that characterize the new production,
financing and marketing processes.3
A second feature of these sectors is the increasing
“industrialization” of agriculture, in the sense that is progressively dependent on non-
agricultural inputs (The Economist 2016; Anlló, Bisang and Campi 2013, Giovannucci et
al. 2012, and Committee on Twenty-First Century Systems Agriculture 2010). The firms
that control critical inputs become the central actors in the value chains. For example, the
“life sciences” companies that supply agrochemicals and transgenic seeds are central actors
in the production of grains, whereas in some fruit and vegetable chains the process is
increasingly controlled by supermarkets and fast food companies in what has been called
the supermarket revolution (Reardon et al. 2009). These companies increasingly determine
the technical package that their preferred suppliers have to use, although many aspects of
the package are developed by independent firms. For example, supermarkets sometimes
offer technical advice to farmers, but the irrigation equipment and greenhouses are
developed and sold by independent companies.
These trends in society and technology also create new opportunities for small farmers. For
example, the number of small farmers in the U.S. is growing strongly to supply the
“Locavore movement” - the consumption of locally produced agricultural products - but the
majority of these farmers also has non-agricultural sources of income. In Europe there is a
strong culture of consuming local foods, a trend that is growing thanks to the development
of new communication and marketing technologies. Similar processes are happening in
middle-income countries, where small producers use modern methods to supply the high-
income urban consumers. For example, farmer markets that sell organic produce are well
established in the city of Buenos Aires.
Finally, around the world small farmers are becoming differentiated into two groups. Less
than 10% has the potential to become commercial producers; the rest is reducing its reliance
on agriculture and depending more on other sources of income, especially remittances sent
by family members who migrated abroad or to urban areas. These farmers cannot become
entrepreneurs and keep their farms to cover their basic food needs and as a retirement
insurance. In time, when they are no longer able to farm and their children have moved to
urban areas, they will sell the land to more entrepreneurial farmers. More than technical
advice, these households need two types support: for younger people, support to develop
capacities that help them integrate into high-wage labor markets, and for the older people
safety nets in the form of a pension or cash transfers (Ekboir and Rajalahti 2012). In
3
This is not a new phenomenon. In the 1940s Schumpeter (1943) introduced the idea of creative destruction
and Cochrane (1958) spoke of the technological treadmill that forced farmers to invest permanently.
7
Argentina these households are probably the largest group of farmers but account for a very
small proportion of the national agricultural output and the total agricultural land; in other
developing countries they are a large but decreasing share of the population.
This last group has received special attention in almost every country. For instance, the 2014
issue of FAO’s flagship publication, the State of Food and Agriculture, dealt with
innovation in family farms (FAO 2014). A major problem with the category “family farms”
is that it has not been well defined. For example, FAO’s publication mentioned that family
farms produced more than 80% of the global food supply; they arrived at this estimate by
including in the category family farms from the North American Midwest who farm
thousands of hectares with precision agriculture and African farmers planting less than 1
hectare. As the rest of the publication focuses on poor small farmers, it is implied that most
of the food is produced globally by poor rural households.
4 What is an innovation?
The literature on innovation systems exploded in recent years, but few papers discuss
thoroughly what an innovation is and how it differs from the traditional notion of technical
change. In this article, an innovation is defined as anything successfully introduced into an
economic or social process (Ekboir et al. 2009). This definition has several important
implications. First, it emphasizes that an innovation is not only trying new things, but that
these ideas or products are used by innovators in processes that include technical, economic
and social components (Brynjolfsson and McAfee 2014). This observation highlights the
central role of individual and organizational capacities in innovation processes (see Sections
10.1 and 10.2).
Second, the definition emphasizes that innovation is not a discrete and finished product. In
the traditional framework, farmers passively adopt technical packages without changes,
such as improved seeds. This simplistic approach of innovation does not represent the
development and diffusion most important innovations as no-till (Ekboir 2003b), mobile
telephony, internet commerce and computers (Christensen 2003).
Third, the definition implies that innovations are contextual (Bailey and Ford 2003). In the
1990s many small farmers in Ghana began to use a long pole placed at the end of the plot.
The pole enabled them to plant the crops in rows facilitating weed control (Ekboir, Boa and
Dankyi 2002). On the other hand, advances in biometrics were not innovations for these
farmers because they could not use them, in the same way that the stick was not an
innovation for mechanized producers. In other words, except for science and art, an
innovation does not have to be new for the world or even for the country in which it is
developed, but only for the agent that incorporates it. But to use an innovation, the innovator
normally has to adjust various elements of his productive and organizational package
(Brynjolfsson and McAfee, 2014; Davila, Epstein and Shelton 2006).
In the framework of innovation systems, researchers do not generate innovations but
scientific information, either codified for example, in a scientific article or embodied in a
8
product, such as an improved seed.4
Researchers also generate tacit information that can
only be shared through personal interactions. Scientific information only becomes an
innovation when an economic or social agent uses it to improve what she is doing. The
innovators use many sources of information, including scientific information, but most of
the innovations do not originate in science but in the everyday activities of economic or
social agents and in interactions among actors in the innovation system.
The innovation system is broader than the research system because in the former participate
different types of actors, such as agricultural producers, private companies and government
agencies. The innovation system can be strong even if the research system is weak; this
happens when research organizations are weak but other actors in the innovation system are
strong. Production of vegetables for export in Senegal and melons for export in Central
America are examples of strong innovation processes coexisting with weak research
organizations. The reverse case has also been observed, i.e. weak innovation systems and
strong research organizations, as was the case of the Soviet Union in the 1970s that could
send a satellite into space but could not produce a reliable car.
In the traditional vision of science, also called the linear vision or the continuum of research
and development, all knowledge begins with basic science, expands with applied research
and ends with technological development and adoption (Figure 1).
Figure 1. The linear vision of science.
However, in the study of actual research systems it was found that this stylized picture
represents only a few scientific areas such as chemistry (Freeman and Soete 1997). From
the perspective of innovation systems, innovations are developed mostly by groups of
private actors which may also involve researchers and other actors from the public sector.
More than a continuum, innovation processes look like spider webs. In Figure 2, farmers
interact with other farmers and with suppliers and buyers to exchange information and
resources to experiment and innovate; these interactions are represented by the solid lines.
4
Researchers generate innovations when they develop new methodologies or research instruments that are
used in science.
9
As researchers may participate or not in the process, they are linked to the other actors by
dotted lines. All these interactions are framed by the socio-economic environment,
represented by the blue box.
Figure 2. The innovation system.
In place of a research motivated only by curiosity, as represented in the linear vision (Figure
1), in the innovation systems framework science is defined by two dimensions: the
motivation of the researcher and the consideration of its use (Stokes 1997). Thus, four
combinations are defined (Figure 3).
10
Figure 3. New characterization of science.
These categories are applied to individual researchers, since researchers in the Einstein and
Pasteur quadrant can work simultaneously in the same organization. But each organization
has to define what quadrant best defines its vision and mission.
5 Some features of complex systems important for the analysis of research and
innovation
Complexity theories offer new perspectives for the analysis of research and innovation
processes. In particular, these processes can be featured as complex adaptive systems, also
known as CAS. CAS are complex because a significant number of actors (including
producers, private companies, public organizations, researchers and funding agencies)
participate in them, each trying to achieve his/her individual goals and reacting to the actions
of the other actors.
One of the most important properties of CAS is self-organization (Crutchfield and Schuster
2003; Axlrod and Cohen 1999). Patterns of collective behavior (of groups and of the system
as a whole) emerge from the actions of individual actors, the interactions among them and
with the environment; these patterns do not exist at the level of individuals. Animals and
plants are examples of emerging patterns, since at the most basic level, they are chemical
reactions, which organize spontaneously into proteins, cells, tissues, individuals and
communities. Animals and plants age, but the chemical reactions that are the basis of life
do not. Markets and organizations, are other examples of collective behavior that emerge
from the accumulation of the actions and interactions between individuals.
Due to randomness and self-organization, CAS are essentially unpredictable and cannot be
controlled. Some actors may be more influential than others, but cannot completely
determine the system’s evolution. Even more, in a complex system it is not possible to
predict the results of an intervention, since the outcomes depend on all the interactions
among the parts of the system. For this reason, interventions in the CAS do not seek to
“manage” the system, but increase the probability of occurrence of desired events and
reduce the likelihood of adverse events, in addition to identifying new questions and tipping
points where interventions can have a large impacts (Axlrod and Cohen 1999).
The most important instrument to operate in a CAS is the creation of variation combined
with an effective selection mechanism. In natural systems the variation is random and the
selection is based on reproductive efficiency. This mechanism works because nature does
not have temporary restrictions; therefore, it can explore a large number of variations until
one that is fitter than the individuals that already exist emerges. In contrast, human
interventions change variation and selection in a deliberate manner. An example of this
strategy is plant breeding; the effectiveness of this activity depends on having good
mechanisms to induce variations and select the best individuals that result from the
variations. In the traditional methods, the breeder crosses thousands of lines that he believes
may result in offspring with the desired characteristics (he is influencing variation) and then
uses selection criteria different from the reproductive efficiency (for example, resistance to
a disease) to choose the best individuals. Currently, scientific advances are changing these
processes to increase its effectiveness (see section 8).
11
Due to self-organization and adaptation, no optimal solutions exist in CAS; therefore,
strategies should seek good enough solutions that can only be found with trial-and-error
based on strong learning capacities (Crutchfield and Schuster 2003). An important
instrument to operate on a CAS is a monitoring system that identifies the state of the CAS
at intervals short enough to enable corrective actions to be implemented if necessary.
The literature on management of organizations in CAS differentiate between four types of
processes (Patton 2011):
1) Simple: the relationship between causes and effects are stable and clear to all; the
causes are separable in the sense that each cause has a direct effect and each effect
has perfectly identifiable and separable causes, such as, for example, moving a knob
to turn on the light. In such cases, rational design is the best strategy to manage the
process, i.e., thoroughly analyze the process and implement the best practices
identified from similar processes.
2) Complicated: relationships between causes and effects are not obvious and their
understanding requires some analysis by specialists; the process is sufficiently stable
that interventions normally result in the desired results. An example is a telephone
system that is composed of millions of cables, switches, teams and individuals;
however, the probability that a call will be completed in the first attempt is quite
high. The strategy recommendation is to analyze the process and use good practices
(not necessarily the best).
3) Complex: the understanding of the connections between causes and effects are
always tentative because many causes interact among themselves in relationships
that change over time. The effects are the result of the entire set of causes so that
they cannot be attributed to individual causes or specific interventions. An
ecosystem and human relations are examples of CAS. The most appropriate strategy
to operate in a CAS is exploration, learning and adaptation. The purpose of
interventions is to increase the probability of occurrence of positive events and
reduce that of the negative events.
4) Chaotic: The process changes so rapidly that it is not possible to identify patterns;
in general these processes are short lived and stabilize in one of the other three types.
In these cases, the best strategy is to try to minimize the negative events and conserve
resources until the change slows down. An example is an area after a natural disaster
where natural, social and economic systems are altered.
As they evolve, processes can change of category, for example, when an isolated market
integrates into a global market, it moves from simple to complex. Conversely, it can go from
complex to complicated, as when a vaccine enables eradication of a disease, eliminating the
need of quarantines and collective action for its control. Complexity theories emphasize the
need to use adaptive management and organizational learning; which are discussed in
sections 10.2 and 11.
12
6 Characteristics of research activities
Research has unique characteristics that reduce the effectiveness of traditional management
approaches used in other activities.
1. Research has a greater uncertainty than other activities concerning the likelihood
of obtaining results, their nature and how long it can take for the results to be used
productively. Often research yields outputs that were not expected when the research
started, as was the case with many medications. Furthermore, the total value of the outputs
may be unknown even at the end of the project; for example, even 60 years after it was first
published it is not possible to determine the value of DNA research because it is not possible
to foresee all the applications in which it can be used.
2. More than one approach can be used to study a particular problem and it is not
possible to tell in advance which one is the most appropriate. Similarly, alternative
solutions can exist for the same problem, not all developed through formal research. For
instance, losses in the transportation of fresh fruits can be reduced by improving roads,
creating new packaging or developing sturdier varieties.
3. In some areas of science, most benefits come from the “best” discovery and not
from the total number of discoveries; for example, when several varieties of a crop
adapted to a given ecosystem are released, only the best one will be adopted by farmers. In
such cases, the research outputs that are not used have no immediate value because it is not
a substantial improvement on the best available technology (Huffman and Just 2000). In
such cases, research is represented as a race where the winner takes all.
4. Alternatively, often the benefits are distributed among several research
approaches; for example, there is no single treatment for cancer (Stephan 2012). This
research is represented as a tournament, where the winner takes the largest prize, but there
are smaller prizes for many participants. The analogy of the tournament helps to understand
the segregation of research organizations according to the talents of their researchers. The
best researchers are attracted by the best organizations; the less talented go to organizations
of a second or third tier while those at the tail of the distribution leave research or work in
laboratories of recognized researchers (Stephan 2012). This process is self-sustained if
researchers can move freely; on the other hand, if there is little mobility and researchers
have job stability from the beginning of their employment, a researcher’s ability is a random
process that is resolved when the organization hires her and on which it has very little
control thereafter. As this self-selection process strengthens the initial distribution of
research capacities in the national and international research systems, it is very difficult (but
possible) to transform a research organization. For this reason, recruitment is among the
most important decisions taken by an organization that offers job security.
5. Even research that does not attain the expected results or is not applied in
productive processes can be valuable because it provides useful information to guide
other research projects or it could be valuable in the future. Even “failed” projects provide
information on alternative research strategies, increasing the likelihood of success of new
projects. Finally, results that today do not have application, can prove valuable in a different
socioeconomic context.
13
6. Scientists have more information than the administrators of the organizations in
which they work, because it is very expensive and ineffective to monitor their effort. Given
the uncertainty of research projects, the actual effort exerted by scientists cannot be inferred
from the results obtained. Researchers who work on more innovative or more risky research
have a lower probability of obtaining results than researchers who do more conventional
research. But the former can make a greater effort and of higher quality than the latter,
although they do not “obtain the results sought”. A system of incentives based on the results
obtained discriminates against the most innovative or more risky research, which often
yields the greatest benefits in the mid- and long term. This point is of great importance for
the design of management systems of research institutions (see section 10.5).
7. Scientific productivity, measured by publications, has a very asymmetrical
distribution. One quarter of all scientific papers is published by 2% of the researchers, 50%
of the papers is written by 10% of the researchers and the remaining 50% by the other 90%
of scientists (Stephan 2012; McClellan and Dorn, 1999). Moreover, bibliometric studies
demonstrate that the great majority of scientific papers are never cited, but that does not
imply that they are not important because they can influence other researchers through
channels not captured by these studies. A system of incentives that prioritizes publications
excludes the great majority of researchers. In systems where the compensation depends on
publications and recognition, the asymmetric distribution of publications results in an
asymmetric distribution of remuneration and of the organizations according to their
reputation.
8. Research is increasingly done by national and global networks involving various
types of partners and partnership arrangements (Wagner 2008); therefore, public
research organizations have to define institutional partnering policies. The need to partner
arises because: (a) equipment is costlier and becomes obsolete faster, (b) the growing
volume of scientific publications prevents a single person from knowing everything that is
published in her area of specialization or the areas she needs for her work, and (c) a large
share of scientific knowledge, especially technical, is tacit, i.e. it is not codified in a book
or model, which forces researchers to communicate with experienced colleagues.
9. The productivity of a researcher depends on his recognition among peers and that
of the organization in which he works. This process is known as the “Matthew Effect”
where the best known researchers receive more recognition for their research than less
recognized ones who do work of similar quality (Stephan 2012). One of the causes of this
effect is the enormous amount of scientific material that is published, which leads
researchers to prioritize reading papers written by recognized professionals. The quality of
researchers is also influenced by the organization in which they work because (a) its
evaluation systems influence the quality of research, (b) the quality of its equipment and
facilities affect the work that can be done and (c) the interaction with more active colleagues
induces a greater effort.
10. The productivity of the researchers who studied abroad or who developed
extensive international networks is higher than that of researchers who only
participate in local networks (Gibson and McKenzie 2014; Wagner 2008).
14
11. Even though research is the quest for novelty, disciplines are conservative. It is
harder to obtain funds to study new phenomena because the probability of success in newer
areas is lower than that of better-known areas. Additionally it is more difficult to publish in
the best professional journals results that differ from the consensus of the discipline because
reviewers tend to reject ideas that do not fall within the disciplinary paradigms. Finally, the
researchers exploring the frontiers of science risk becoming isolated from their
communities, which is a problem in an activity that is increasingly a social enterprise. On
the other hand, when the most innovative research is successful, it entails great benefits in
the form of fame and funds (Von Krogh et al. 2012).
7 The relationship between scientific research and innovation
Science has been defined as what scientists do, while technology is what technologists do
(Goldfarb 2008; Stoneman 1995). This definition emphasizes that the difference between
science and technology does not lie in what the professionals do but on why they do it and
what criteria they use for accepting knowledge as true. That is to say, it is recognized that
these activities are social processes.
Traditionally, scientists’ ultimate goal has been to create new knowledge that was
disseminated freely and as quickly as possible through specialized media. In other words,
the goal of scientists was the creation of information, a public good.5
In contrast, for
technologists, research has been a means to obtain private profits by creating private goods.
The race to decode the human genome exemplifies these differences. Two teams of
researchers participated in this race; a team was coordinated by a private company and the
other by an international consortium of public institutes. The private company wanted to
patent knowledge while the public researchers sought to publish their findings as quickly as
possible. Both teams researched the same phenomenon (although with different
methodologies) and obtained similar knowledge, but one was doing science and the other,
technology.
The Bayh-Dole act enacted in 1980 allowed private commercialization of results obtained
by publicly funded research. This act together with new technologies that spurred the
development of commercially valuable science-based products blurred the distinction
between science and technology. Before the changes it was generally accepted that public
and private researchers should work as separated as possible to avoid “polluting” research
agendas with commercial interests. It is now recognized that public-private interactions help
researchers to focus their work on relevant issues, as well as identify new problems and
understand the requirements of users and market trends. However, partnerships with
industry can distract researchers from long-term research and reduce the dissemination of
research results in order to protect trade secrets or because the results are not sufficiently
innovative (Perkmann and Walsh 2009). Different studies found that the increase in
5
A good is public if it is non-rival and non-excludable. Non-rival means that consumption of the good by an
agent does not affect the quantities available to others. Non-excludable means that no agent can be prevented
from consuming the good if she wants to. Open television is a public good because the fact that a person
watches TV does affect other people who want to watch it (non-rival) and because there is no way to prevent
any person with a TV set to watch it (non-excludable). The public nature of a good does not depend on whether
it is produced by a public or a private company, but on the two properties just explained.
15
patenting by universities did not restricted most researchers’ access to materials mainly
because patents were not respected. However, important cases have been documented in
which the private ownership of critical research inputs is restricting their use, for instance,
the control of stem cell lines by the University of Wisconsin or genetically modified mice
by Dow Chemical (Stephan 2012). Also, there is anecdotal evidence that the exchange of
germplasm of the most important crops between public programs from different countries
has declined since the enactment by many countries of plant variety protection laws.
Similarly, the assessment of a partnership between the University of California and Novartis
found that the university administrators and researchers involved in the partnership tended
to define public goods as research resulting in marketable products, which changed the
nature of public goods (Vanloqueren and Baret 2009).
The privatization of research outputs obtained in public universities is having another
important effect on science. For many centuries, researchers sought to be recognized as the
first to report a discovery; in other words, to own a discovery researchers had to give it away
by publishing it, which fostered scientific progress. As research began to have commercial
potential and universities sought to exploit it, the incentives for delaying the publication of
results strengthened (Stephan 2012).
The other difference between science and technology is the criteria to accept knowledge as
valid. For science, knowledge is valid when it “explains” the phenomenon under study and
the same results are obtained independently by several researchers. In contrast, for a
technologist, the criterion is that the knowledge solves a problem; understanding the
underlying processes is a secondary objective. These differences are important because they
determine in part the incentives offered to researchers.
In addition to the differences between science and technology at the level of individual
professionals, the literature of innovation systems offers new perspectives on the role of
research at the level of organizations and countries. In accordance with the linear vision of
science, private companies invest in research less than the social optimum because the
research outputs are public goods, i.e. information which once disseminated can be used by
anyone for free and without affecting its use by other users. As private actors cannot charge
for the use of the scientific information they generate, they do not have incentives to invest
and the public sector should cover the deficit, normally in research removed from
application such as basic science. In contrast, from the perspective of innovation systems,
the public nature of scientific information is not the main cause why companies do not invest
in research; the key reason is that to be able to use scientific information organizations have
to make large investments for long periods to develop capacities that enable them to
understand and absorb it (Cohen and Levinthal 1990). In other words, although the
information is free, its use is not and depends on the capacities developed by the
organization. From this perspective, scientific information is similar to nitrogen, which is
the most abundant gas in the atmosphere but has no economic value unless it is processed
in large plants.
According to the literature on innovation systems, in addition to addressing the public nature
of research outputs, science and innovation policies must foster collaboration between
16
different types of actors, reduce long-term uncertainty and support the development of
emerging markets (Lundvall and Borras 2005). Many countries, including Argentina, have
implemented important programs to develop technology-based firms and to promote
innovation in the agricultural sector. However, anecdotal information suggests that few
researchers have changed the way in which they interact with nonacademic actors.
The criteria for defining the quality of research have also changed in recent years. According
to the linear vision of science, quality was defined solely by peers within the same
discipline. In contrast, in the innovation systems framework, quality is also defined by its
interdisciplinary nature and relevance to society. The problem is that there are no universally
accepted criteria for judging these new dimensions. In the case of evaluation by peers, it is
accepted that these have adequate knowledge of the discipline to be able to judge the quality
of the work and that this system works in most of the cases.6
But it has been more difficult
for researchers to evaluate the quality of inter and transdisciplinary work as individuals are
experts only in one part of the research and often do not understand developments close to
the disciplinary boundaries. Finally, there are no criteria for assessing the economic and
social relevance of research, especially when the evaluators are not scientists (Frodeman
2012). These problems have been recognized in the literature, but no solutions have been
found.
8 Trends in the organization of science
8.1 How the rapid advance of science affects research organizations
The volume of scientific information is growing so quickly that no researcher can read
everything that is published in her area of specialization. Twenty years ago the problem was
how to access an updated collection of scientific journals; the problem today is to find
relevant information in a sea of useless material. In a recent work, IBM estimated that to
follow everything that is published in his specialty, a doctor needs to read 160 hours per
week (Brynjolfsson and McAfee, 2014). This phenomenon has seven important
consequences.
1. The impact of research depends on its quality and the reputation of the researchers
(Stephan 2012; Wagner 2008). New information can be lost in the vast volume of
publications unless someone discovers it, and it is easier to be discovered if a famous
researcher is one of the authors of the publication. This point is important for the
design of systems to evaluate organizations and researchers (see section 11.2).
2. Most of the results of research is “lost” if it is not read by other researchers,
regardless of its quality or potential usefulness. To prevent this from happening,
organizations and researchers have to develop new capacities to disseminate the
results of their research (see section 0).
3. As they do not know everything that is published in their disciplines and in other
areas of research which may be relevant, researchers have to collaborate with other
6
Some authors have shown that evaluation by peers is too conservative (Siler, Lee and Bero 2015; Axlrod
and Cohen 1999).
17
researchers (see section 8.6). Sometimes these collaborations result in new
disciplines that emerge at the boundaries between disciplines, for example,
bioinformatics (Weingart 2012; Heinze et al. 2009). Funding agencies of several
countries are actively promoting interdisciplinary (between disciplines) and
transdisciplinary partnerships (which also include actors who are not scientists). For
example, the National Institutes of Health of the U.S. allocated a third of their budget
to a program of translational research meant to accelerate the translation of basic
research results to improvements in medical and health care (Stephan 2012).
4. Traditional academic departments structured by disciplines are starting to encourage
more interdisciplinary approaches, both in research and teaching (ICSU 2011).
Some U.S. universities (for example Arizona State University and University of
Arizona) have already introduced interdisciplinary curricula where students study
by means of case studies and not following structured academic programs.
5. Researchers in areas that change rapidly, such as biotechnology, can become
technically obsolete quite quickly. This creates significant challenges for the
organizations in which they work because it is necessary to implement programs for
continuous learning, either by training the researchers, hiring new professionals or
introducing more flexible working mechanisms, for example, organized around
specific problems and transdisciplinary collaborations.
6. The number of research outputs produced and marketed is increasing at an
increasing pace (Friedman 2016). This has two important consequences. Firstly,
research organizations and their researchers have to invest increasing amounts of
resources to remain updated. Secondly, the commercial life of many products is
becoming shorter due to the emergence of more modern competing products. These
features are increase the cost and risk of many research areas, which induces firms
to merge.
7. Public research organizations have to maintain the lines of work for several years
because research needs time to bear fruit and because researchers have stability in
their work. At the same time, due to the rapid progress of science and technology,
national research systems and the organizations in them have to adapt faster than in
the past. The balance between continuity and change requires a delicate balance
because decisions about what to research and how to do it are partially irreversible.
This problem is enhanced by the difficulties that the public sector has to end ongoing
projects due to the interests that would be affected by the decision.
8.2 New areas of research have emerged, but most resources are invested in a few
areas (TICs and life sciences, especially related to human health)
Transdisciplinary research is implemented by groups of researchers from different
disciplines who interact actively with nonacademic partners, such as farmers, traders and
equipment manufacturers. It should be noted that interaction is not equivalent to
participatory research; in fact, there are no studies that demonstrate that participatory
18
research is effective.7
Transdisciplinary approaches are becoming more common due to the
factors mentioned in the previous sections. This phenomenon, called “Convergence 2”
(Roco et al. 2014), began to be recognized as a trend in the interaction between synthetic
biology, ICT, artificial intelligence and nanotechnologies; it was later extended to virtually
all scientific areas. Other emerging research areas are gene editing and the manipulation of
regulatory mechanisms in cells (including CRISPR and RNA interference), cloud
computing and Big Data applied to production systems and complexity theories (Axlrod
and Cohen 1999). These changes are redefining research, technology and socioeconomic
processes in all scientific areas. For example, editing DNA, scientists can block essential
processes of specific pests and diseases, creating herbicides or insecticides that only affect
the individuals they seek to control.
The new areas of research are forcing changes in the more traditional ones, such as plant
and animal breeding, where molecular markers, bioinformatics, advanced models of gene
expression, remote sensors and editing of genes are combined with traditional plant
breeding (Cooper et al. 2014). But the traditional disciplines will still be necessary. For
example, biotechnology can introduce new features in seeds, but for farmers to use them,
these features must be incorporated into good varieties. Researchers in the new and
traditional areas will have to learn how to interact and managers of research organizations
must develop the capacity to navigate processes that are more complex from a technical,
social, political and legal point of view.
8.3 Increasing privatization of the cutting-edge research in some areas
Private investments are increasing in several research areas with important implications for
public research, in both developed and developing countries. Not even the most important
universities in developed countries can compete with private firms for the best equipment
and researchers, which implies that eventually these firms are going to dominate the
frontiers of science in certain areas (Stephan 2012; Kraemer 2006). The dominant position
that private firms are achieving may influence what is being researched and how it is being
done. Increasingly the firms that research areas that interest the agricultural sector,
especially biotechnology and breeding, will become akin to the pharmacological firms. This
could be an important problem if oligopolistic structures arise and if social costs and benefits
differ from the private ones where a few large companies and many start-ups operate side
by side.
So many research areas are emerging that the big life science companies cannot take
advantage of all the new opportunities, which are then taken up by small firms and startups,
but the areas less profitable for the private sector such as minor crops and agricultural
systems are no being tackled by any of these firms.
A major problem of relying exclusively on private research is the difference between
different private costs and also between private and social costs. For example, a few
7
An unpublished extensive bibliographic search conducted by the Institutional Learning and Change Initiative
of the CGIAR in 2010 did not find any publications that showed the effectiveness of participatory approaches.
In fact, all publications assumed that participatory research was the most effective approach. In 2015 IITA
conducted another bibliographic search with identical results.
19
processing companies hired farmers in a valley of the state of Jalisco, Mexico, who
produced tomatoes as monoculture. After some years, the crop was attacked by a virus that
survived in the soil, and therefore tomato production in the area had to be discontinued. The
processing companies moved production to another valley, but the producers in the affected
area lost an important source of income. The problem could have been avoided with a more
holistic production approach supported by transdisciplinary research involving public and
private researchers working with producers.
As no organization can cover all areas of research, from basic research to product
development, private companies collaborate with public and private organizations that
possess capacities they lack. The nature of the organizational capacities is discussed in more
detail in the section 0. Two non-traditional capacities that public research organizations will
need strengthen are intellectual property protection and communications. The protection of
intellectual property is becoming increasingly complex. In fact, privatization of research is
generating what has been called the tragedy of the anti-commons: the excessive
fragmentation of intellectual property rights may slow research and development of
products because each patent owner can block other researchers and because it increases the
cost of understanding what patents may be affected by the research (Dosi, Llerena and Sylos
Labini 2006). In such an environment, how can public research organizations access all the
inputs they need? Who is going to pay the increasing indirect costs of research?
The value of intellectual property protection is being affected by the rapid advancement of
science and technology. A patent creates a monopoly for a specific period but the period
can be shortened if the protected product is rendered obsolete by a more advance competitor;
in such cases, the patent loses its value before its expiration. The high cost of patenting and
enforcing the patent favors large companies with plenty of resources to protect their
intellectual property.
Another issue that can arise from the privatization of research is the possible emergence of
oligopolies in input and output markets. In such cases, the state should regulate those
markets, which requires that the regulators develop strong analytical capacities. However,
many developing countries do not have these capacities. Even in developed countries many
regulatory agencies cannot follow scientific advances, so they request the advice of public
researchers. In order to be able to provide this service, public research organizations need
to transform their systems of incentives and evaluation, valuing other activities in addition
to scientific publications (see section 11.2). A related issue are the conflicts of interest that
may arise if public research organizations play both roles of partners of private firms that
fund their research and advisers of regulatory bodies. In order to avoid this problem it is
necessary to strengthen the technical capacities of regulatory bodies so that they depend less
on public researchers.
Communication specialists will become more important because the social perception of
the benefits and problems that emanate from scientific research has become a driving force
of the directions that science can take. Even more, it is expected that the influence of
communications will continue to grow (Rodhes and Sawyer 2015). Communicators should
20
help to educate non-scientists in the advantages and limitations of scientific research and to
develop effective communication strategies.
Due to the privatization of research, many science-intensive products are distributed
simultaneously across the world, which allows developing countries to access technologies
developed in developed countries without delay (Archibugi and Michie 1998). This reduces
the need for private investments in developing countries in certain areas of research, for
example, ICT and new herbicides. But this strategy is valid only for research outputs that
can be used in different geographies with little adaptation, which is unusual in agriculture
and management of natural resources.
8.4 Public research is more distributed globally but it is concentrated in a few
countries; additionally, private research in developed countries is increasingly
concentrated in a few companies
Overall, research is increasingly concentrated in developed countries and China. The
scientific dominance of the U.S. until the 1990s is being challenged by the European Union,
Canada, Australia and China, followed far behind by a few developing countries such as
India and Brazil (Stephan 2012; Wagner 2008).8
But as the majority of the new scientific
powers have institutional environments and cultures less favorable for the transformation
of scientific outputs in innovations, it is expected that the U.S. will continue to dominate
innovation in the coming decades.
8.5 The number of tenured research positions in both the USA and Europe is falling
while the number of Ph.Ds. is growing
The number of tenured research positions is falling throughout the world, while the number
of researchers with non-tenured jobs is growing. Even more, increasingly the work and
sometimes the salaries of tenured researchers is funded through time-bounded projects
(National Research Council 2014(b); Teitelbaum 2014). The reliance on financing of
projects makes researchers more conservative and less prone to explore lesser known
scientific areas (Stephan 2012). This trend is particularly important for research on complex
processes, such as adaptation to climate change or agricultural systems. In addition, these
financial procedures reduce the incentives that researchers from advanced research
institutions have to collaborate with organizations from developing countries in long-term
projects that do not pay for their salaries.
Finally, a growing number of Ph.Ds. accept postdoctoral positions (sometimes for several
years during which they work long hours for low salaries) with the hope of eventually
achieving a tenured research position. But as the number of these positions is falling, many
postdocs end up in non-academic jobs (Stephan, 2012; Lieff Benderly 2014; Teitelbaum
2014). The imbalance between supply and demand of scientists is an opportunity for
8
The funding of research in Brazil has been severely reduced in the current crisis.
21
developing countries willing to invest in their research systems. But this requires long-term
commitments, major investments and appropriate incentives.9
8.6 Science is increasingly conducted by international networks
Researchers from different organizations and countries are increasingly collaborating with
each other (Wagner 2008). Networks help researchers to exchange tacit knowledge and
access resources, especially specialized equipment (van Rijnsoever et al. 2008; Kraemer
2006). But the majority of researchers from developing countries has few resources to
participate in international networks, for example, taking part in professional meetings or
visit advanced research organizations (Wagner 2008). Another way to participate in
international networks is to invite foreign researchers and facilitate access to local research
projects and contacts with local researchers and policy makers.
As networks grow, reputation becomes critical to attract resources and collaborations but
researchers from developing countries often do not have enough resources to increase their
visibility in professional fields (Wagner 2008).
8.7 Financing of time-bound projects is increasing relative to blind grants
Thirty years ago public research was financed mostly through blind grants (non-conditioned
direct transfers). Over time, these grants were replaced by financing of time-bound, well-
defined projects (Kraemer 2006). Studies of research financing found that, as a result of this
change, (i) the volatility of funding has increased, (ii) discontinuities in political processes
conspired against long-term research; (iii) it has been more difficult to develop strategic
partnerships; (iv) researchers spent more time searching for funds and preparing reports,
which reduced the time available for research (Teitelbaum 2014; Stephan 2012; Heinze et
al. 2009; Vera-Cruz et al. 2008; Wagner 2008). In addition, as reputation matters for fund
raising, funding is increasingly concentrated in the more established researchers, forcing
young researchers and those from developing countries to depend on senior researchers.
Stephan (2012) reports that researchers funded with long-term blind grants are more
productive and creative than researchers funded through projects, provided that quality
controls are enforced.
9 Changes in extension systems
The role of extension changed over time and geographies. Countries with dynamic
agricultural markets established extension systems more than a century ago but in recent
9
The magnitude of the imbalance between the supply and demand of highly trained scientists, particularly in
the U.S., is a highly politicized issue because it covers immigration policies and support for firms in science-
intensive areas such as biotechnology and information technology. As a postdoctoral position is considered
academic training and not a stable job, postdocs receive lower salaries than more established researchers who
do the same work. The associations of postdocs and some researchers accused universities and funding
agencies of using postdocs as cheap labor instead of recruiting established researchers with more job security
and higher salaries. Recently, the U.S. government ruled that postdocs are workers (and not trainees) and
therefore are entitled to overtime compensation. Although it is expected that this ruling will have important
impacts on research activities, there is no consensus on what they will be.
22
years the changes in agricultural systems and in the organization of science changed the
demands on extension, which generated a crisis that has not yet been resolved.
Since its inception in the nineteenth century in the USA and Europe, the mandate of public
extension organizations was to help farmers to adopt more productive technologies
developed in public universities. Even more, as many of the research carried out in the
universities still required significant adaptation before it could be used by farmers, a
network of state experimental stations was created in the USA. As it was also accepted that
researchers could not research and interact with a large number of farmers, in 1914
cooperative extension systems were created at the national, state and county levels.
The second half of the XX century was characterized by a steady expansion of farm sizes
and the privatization of the production of some inputs, like seeds. As a result, the demand
for public extension by commercial farmers producing traditional products (mostly grains,
milk and meat) shrank. In 1972 two reports strongly criticized public research and extension
systems. The Hightower report argued that these systems were too focused on agricultural
firms and not sufficiently on family farms. The Pound report complained that public
universities were not doing enough basic research, that their science was not good and that
they did not contribute to the future of agriculture. The conclusion of the Pound report was
that researchers were too “near” extension agents and did too much applied research, adapted
to local conditions. In the following years most agricultural universities expanded research in
biotechnology and other cutting-edge areas and reduced the more traditional research in
agricultural products and production systems. This shift was reflected in the reduction of the
agricultural departments and the dismantling of the majority of the plant breeding programs.
Extension organizations have changed in response to changes in the sources of information
used by farmers. Today most commercial farmers rely increasingly on private advisers,
often linked to the sale of traditional inputs, e.g., fertilizers, and more recently to the
purchase of specialized services (such as the analysis of data in precision agriculture), direct
communications with researchers and recommendations from companies. At the same time,
government support for extension weakened, due to the decrease in the number of farmers
and rural laborers (i.e., they became a smaller political constituency) and the limited support
extension agents could provide to “non-traditional” small farmers that supply more
sophisticated local markets, for example, organic producers (Buttel 1991). The new
environment resulted in a sharp decrease in the number of extension agents. Today
extension in public universities in the U.S. interact mostly with professional who advise
commercial farms, and occupy niches that private advisers do not occupy, for example,
disseminating information on the management of natural resources. But the future role of
public extension is being questioned and the state extension services are still trying to define
their niche (Al-Kaisi et al. 2015).
In the Netherlands public extension evolved from disseminating technical information
generated in Dutch universities to help farmers to search for scientific and technical
information and foster farmer organizations. At the end of the 1980s extension was
privatized in the sense that extension agents lost their status as public employees (Eicher
2006). Extension in Norway followed similar paths (Eicher 2006). Since the 1990s,
23
following research on innovation systems, several European organizations began to foster
interactions between different actors in the agricultural and industrial innovations systems
(Klerkx and Leeuwis 2008).
Public extension in developed countries faces six major problems:
a) Its traditional customer base (large commercial and “progressive” farmers) is shrinking
because farmers are becoming more professional, information is more easily available,
and these farmers prefer to consult with highly specialized private advisers or directly
with researchers (Buttel 1991).
b) The public extension systems were designed to disseminate public and non-proprietary
information generated in public research institutions but an increasing amount of
information is private and patented (Buttel 1991).
c) As farmers integrated into markets for inputs and outputs, the economic and financial
dimensions of agricultural production increased, but public extension agents usually do
not have the required training to advise on these issues.
d) New actors such as public regulatory agencies require help to access and understand
scientific information because they have less information than the firms they have to
regulate; however, extension agents usually are not prepared to work with regulators,
especially at the state and local level.
e) Farmers need impartial technical information to be able to compare commercial inputs.
To generate this information, research organizations have to conduct unbiased
experiments; similarly, extension agents have to be impartial when disseminating
information, which requires that the research and extension systems do not have
conflicts of interest. This problem is similar to a sick person who has to decide what
medicine to buy; it is assumed that doctors provide an unbiased service, but it is not
always so since many cases of doctors having conflicts of interests have been
documented.
f) The Big Data revolution is revolutionizing farm management; it has been difficult for
public extension agents to follow these developments which are mostly done by private
firms.
Another challenge for public extension services in several countries, such as France, Brazil,
Bolivia, Paraguay and Argentina, has been the emergence of farmer organizations that
validate and disseminate technical information. These organizations are managed and
financed by farmers. Often the interactions between public extension systems, private firms
and producer organizations have been weak.
Important changes in agricultural extension organizations have been introduced in
developing countries since the beginning of the XXI century. Following the limited impact
of the traditional extension models, most developing countries closed their public extension
services, which were replaced by a variety of arrangements (Christoplos 2010). At the same
time and following the increasing acceptance of the innovation systems framework,
governments and donors recognized that small farmers needed not only technical advice but
also support in a variety of issues such as education, health and access to markets. Thus,
24
rural advisory services became the new paradigm for extension, which involved more
activities than just transfer of technologies.
Despite the many arrangements tried in many countries, no cases have been documented in
which public advisory services have played a major role as development agents. Instead,
this role has been played by local and international NGOs (McChrystal et al. 2015; Deaton
2013), each one specialized on specific topics, such as education, health and access to
markets. The most important drivers of this trend are:
 There are many types of family farms with many types of needs; no organization
can cover all these topics.
 As agriculture is losing weigh in the livelihood strategies of poor rural households,
adoption of more productive technologies is not among their main priorities (Ekboir
2016); in other words, agricultural technology transfer is not the main priority in the
new packages of rural advisory services.
 Most professionals in the public extension services do not have the professional and
non-professional background to advise on the new priority areas.
 Public extension agencies are still experiencing the same problems they faced years
ago, i.e., insufficient budgets, lack or resources, low salaries and lack of training,
compounded by more than 20 years of institutional decay.
 After the failed experiences of the 1980s and 1990s, most donors lost confidence in
public extension agencies and they prefer to finance NGOs which are more flexible
than public organizations and respond faster to donors’ priorities.
 Market led development projects, i.e., projects that seek to integrate small farmers
to markets, have not been very effective, because it has been estimated that less than
10% of these farmers have been able to continue their commercial activities after
the external support ended (Ekboir y Rajalahti 2012).
Even more, not even the CGIAR centers have been able to maintain their relevance in
support to small farmers, even though they pioneered many of the methodologies to work
with these farmers that NGOs adopted later. The reason why the international centers could
not keep their central role is that they defined themselves as global research actors which
were not supposed to work at the national or local level; once the NGOs started to develop
capacities to manage this type of projects, they were sought by the donors and displaced
other actors in development projects.
10 Framework for the organizational analysis of research organizations
The analysis of organizations followed advances in scientific theories, especially systems
analysis in the 1960s and theories of complexity in the 1990s (see section 5). The
mechanistic understanding of organizations, based on systems analysis, described
organizations as deterministic mechanisms that optimize their operations subject to
restrictions that they cannot change. The organizations and the environment in which they
operate are seen as predictable and simple and therefore management should be based on
thorough detailed planning supported by “scientific” studies. When implementation departs
25
from what was planned, the organization should invest more resources to correct the
deviations. This management model is known as “command and control”, or in its more
modern version, results based management (McChrystal et al. 2015).
The alternative vision, also called the vision based on resources, represents organizations as
complex adaptive systems and uses metaphors of the biological sciences, in which
organizations resemble living beings that respond to the signals they receive from a
changing environment. i.e., the paradigm of efficiency is replaced by that of adaptability
(McChrystal et al. 2015). Although efficiency continues to be important, adaptability
becomes the most important criterion.
In this framework organizations pursue their goals using their idiosyncratic resources and
the capacities they developed. The organizational capacities arise from the interplay
between resources (individuals and fixed capital), processes (the formal and informal
mechanisms through which the organization manages its activities) and values (including
priorities, the institutional culture and long-term goals). In new organizations most of the
capacities reside in the resources, especially its people. The hiring or departure of a key
person can have great influence on the organization. With time, successful organizations
transfer their capacities to processes and values (Christensen and Raynor 2003).
Internally, adaptive organizations invest time and effort to (a) ensure that their members
internalize the mission, vision and organizational culture; (b) develop channels of
communication among its members, and (c) establish a more horizontal hierarchy where
each member has some autonomy to make decisions. Externally, these organizations form
networks with other actors. This management model has been adopted by the most
innovative companies in the world (for example, Google, Facebook, Whirlpool and GE) as
well as the U.S. armed forces in the Middle East (McChrystal et al. 2015).
Another important shift in the theory of the organizations was the recognition that
organizations of economic and social importance include not only private companies but
also public, non-governmental, private non-profit, community-based organizations in
addition to different types of networks. Each type of organization has a specific structure
that results from its objectives, cultures and histories; no structure is inherently better than
the alternatives.
In the new framework, an organization is defined as a group of actors who collaborate for a
sustained period of time (Ekboir and Rajalahti 2012). The actors can be individuals, firms
or any other type of structure; organizations can be formal, as a company, or informal, such
as a network or community of practice. In a similar way, an organization can be defined as
a coalition whose members and stakeholders seek to maximize their individual interests
(van Rijnsoever et al. 2008). From the sustained collaboration, the members of the
organization develop a common culture, communication codes, incentives, routines and
governance arrangements. These elements define to a certain extent what the organization
does and how it does it.
Five special features of public research organizations are that:
26
 There are no clear measures of success; while private companies seek profits, non-profit
organizations’ objectives include social welfare and economic development which are
harder to measure (Hudson, 2009; Fukuyama 2005);
 Usually, research outputs are obtained after several years and outcomes become visible
long after the research ended (Ekboir 2003a);
 It is difficult for research organizations to change their lines of research due to public
sector regulations, especially the management of human resources;
 If stakeholders and the legislative and executive branches cannot define together what
they expect the research organization to do, its objectives and mandate emerge from
complex interactions between many actors from different sectors (Fukuyama, 2005);
 Usually it is not possible to establish a simple and direct connection between research
activities and their impacts (Ekboir 2003a).
Due to these features, it is necessary to define new approaches to manage research,
especially the incentives and monitoring and evaluation systems (Mayne and Stern 2013;
Hudson 2009).
10.1 Organizational capacities for innovation
A major shift in the analysis of organizations has been the recognition of the importance of
organizational capacities for innovation, i.e. the skills to integrate internal and external
resources to address emerging opportunities or problems (Ekboir and Rajalahti 2012).
Innovation capacities depend on both individual (for example, creativity) as collective
attributes, including routines, collective learning mechanisms and organizational cultures.
Innovation capacities cannot be bought but have to be developed through strong leadership
and sustained investments. But it has been difficult for public organizations to sustain such
programs because its management changes relatively often, they struggle to keep their
professionals up-to-date and have restrictions for committing to multi-annual budgets that
depend on political decisions. This does not mean that these organizations cannot implement
capacity development plans but in order to do so, they have to define long-term master plans
divided into independent modules. In this way, it is possible to implement activities limited
in scope that contribute to the long-term objective.
Organizations cannot develop their capacities arbitrarily and must follow certain paths;
therefore, the organization’s history not only defines the current options but also sets limits
to the options that will open in the future. In other words, the long-term capacity
development plans are largely irreversible (Teece, Pisano and Shuen 2000).
It is useful to distinguish between technological and organizational capacities, although the
two overlap in the real world. The former relate to scientific and technological knowledge,
and routines to manipulate nature (for example how to transform a piece of metal into a
particular structure). The organizational capacities, in contrast, are shared elements of
knowledge and routines concerning governance, coordination and social interaction within
the organization and with external entities (e.g., suppliers or customers). The organizational
capacities are fundamental in the definition and implementation of the so-called “business
27
model”, i.e. the mechanisms by which an organization seeks to achieve its objectives. All
organizations, even non-profits have a business model, although it may not be explicit.
The organizational capacities reflect the collective capacity to perform certain tasks. These
capacities have several components, including routines, the ability to learn (including the
capacity to change routines) and the organizational culture (Dosi, Nelson and Winter 2000).
In addition to being an essential part of the institutional memory, routines reflect governance
structures, compromises among divergent interests, internal control methods and relatively
decentralized decision-making mechanisms. Essentially, routines are conscious or
unconscious agreements emerging from conflicts of interests within the organization (Coriat
2000). Sometimes routines are so complex and tacit that the organization itself is not aware
of their existence or they do not understand them; for this reason, it is very difficult to
change existing routines without decisive interventions by managers to align the
organization on the change process.
Capacities are specific to each organization – they are developed from investments and
idiosyncratic processes. Because it is difficult for other organizations to copy or buy them,
some organizations succeed where others fail. For example, the Xerox laboratory in Palo
Alto developed in 1973 a personal computer with a graphical interface and mouse (copied
by Apple), an operating system that could use several applications simultaneously (sold to
Bill Gates who used it as the basis for Windows), Ethernet connection and the first
WYSIWYG word processor, i.e., a word processor that showed on the screen how the
document would look when printed (Carayannis, Gonzalez and Wetter 2003). But Xerox’s
culture, focused on photocopies and large computers did not allow it to see the potential of
these discoveries and to take advantage of them. Capacities are contextual; capacities that
confer advantages in a given context can become disabilities when the environment in which
the organization operates changes (Christensen 2003).
The allocation of resources is one of the most important processes in any organization and
influences the creation of capacities. In a complex organization there are two mechanisms
for allocating resources. The deliberate allocation is the one decided by the organization in
its planning process. In contrast, the emergent allocation results from the accumulation of
all the decisions made continuously by all members of the organizations from the top
management to the lowest employee. Less hierarchical employees decide which actions
prioritize (for example, which farmers to visit), the mid-level managers decide which
options they will present to their supervisors (removing the options that they believe the
supervisors will not value), and the top executives decide the overall strategy, partly in
response to the feedback they receive from the employees (Christensen and Raynor 2003).
The combination of these processes determine the real strategies because they result in the
actual allocation of resources. One of the most important responsibilities of the
organization’s senior management is the identification of the emerging strategies and
compare them with the deliberate strategies. The differences indicate problems or
opportunities that the organization could address.
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations
A Framework For The Institutional Analysis Of Research Organizations

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A Framework For The Institutional Analysis Of Research Organizations

  • 1. INVESTIGACIÓN, DESARROLLO E INNOVACIÓN A framework for the institutional analysis of research organizations INTA Javier Ekboir Serie: Documentos de trabajo del CICPES. Nº 10/2016
  • 2. Javier Ekboir A framework for the institutional analysis of research organizations INTA Centro de Investigación en Ciencias Políticas, Económicas y Sociales 2016
  • 3. A framework for the institutional analysis of research organizations INTA Investigadores: Javier Ekboir1 INSTITUTO NACIONAL DE TECNOLOGÍA AGROPECUARIA Centro de Investigación en Ciencias Políticas, Económicas y Sociales Tel: 4338-4600 Av. Rivadavia 1439 (C1033AAE) C.A.B.A. - Argentina 2016 Coordinador Editorial: Gabriel Delgado Editor: Ana Laura Schonholz Diseño de tapa e interior: Mariano Mancini ©, 2016, Ediciones INTA Esta publicación es propiedad del Instituto Nacional de Tecnología Agropecuaria - INTA Domicilio Legal Rivadavia 1439, cp. 1033, CABA Propiedad Intelectual: En trámite Serie: Documentos de trabajo del CICPES. Nº 10/2016 1 INTA - CICPES Como citar este documento: Ekboir, J. (2016). "A framework for the institutional analysis of research organizations INTA". Serie: Documentos de trabajo del CICPES. Nº 10/2016. INTA. Ciudad Autónoma de Buenos Aires: Ediciones INTA. ISSN 1514-0555.
  • 4. Executive Summary The environment in which public research organizations operate is changing at an increasing pace, creating the demand that they to adapt. However, these organizations are structured to operate in relatively steady environments and they do not have the tools to function in situations that are in constant flux. This paper presents a framework for conducting an institutional analysis of public research organizations; the framework is based on novel science and innovation policies and the trends that have recently been documented in the organization and management of science. A second document, prepared within this project, contains the results of a study that used this framework to analyze Argentina’s National Agricultural Technology Institute (INTA). a) The framework for the institutional analysis of public research organizations The framework for the institutional analysis of public research organizations characterizes them as complex organisms that evolve in response to external and internal factors. These factors can be grouped into motivations and capacities. The main motivations for public research organizations are the desire to contribute to social wellbeing and the advancement of science, external pressures (from both the policy makers and budget allocations), and scientific progress. Capacities determine how organizations respond to new challenges or opportunities. The capacities are built through sustained investments and depend on several factors, including the history of the organization, its culture, governance, the presence of innovative individuals and the environment in which it operates. The influence of each factor changes over time, so that variables that have a positive effect at a given moment can become negative factors later. The evolution of organizations is also influenced by the mechanisms they use to define their strategies and to implement them. All organizations have two simultaneous strategies: deliberate and emerging. Deliberate strategies are those formally defined by the organization while emerging strategies result from the operational decisions of each member of the organization. The accumulation of these operational decisions result in the actual allocation of resources, which may differ from the allocation decided in the deliberate strategy. Operational decisions tend to be tactical and include, for example, the allocation of resources within programs or deciding which farmers to visit. Without effective leadership and learning mechanisms, emerging strategies dominate the evolution of organizations, preventing them from adapting to emerging opportunities or problems. Organizational capacities are built through sustained investments, strong leadership and careful selection of those responsible for carrying out the capacity development activities. It is also essential that the senior management (a) help to install a shared vision of the changes that will be implemented, (b) allow an adequate number of pilot projects to reduce uncertainty and create mechanisms for effective feedback and (c) promote discussions internal to the organization and with stakeholders to agree on what is desirable and acceptable by the organization. Three of the most important characteristics that influence organizational innovation capacities are the learning mechanisms (because they determine how quickly new
  • 5. information can be generated and used to improve the organization’s activities), the organizational culture (because it determines how “unconscious” factors influence organizational change) and the governance arrangements (because they define how flexible the organization is when organizing its activities and resolving conflicts). Typically it is difficult for organizations to learn because (a) they tend to prefer routines that have so far been successful to new approaches that could be more effective; (b) in order to obtain results consistently they cannot permanently change what they do and how they do it; and (c) directors, managers and employees tend to be absorbed by routine activities. Organizational learning includes the processes by which knowledge is created and distributed in the organization and is integrated into its operations. Organizational learning is a lengthy process that only happens when it is strongly promoted by the organization’s decision makers and when appropriate incentives are introduced. The organizational culture is a set of assumptions, values and beliefs developed by an organization. Some cultures facilitate individual learning while others encourage collective learning; similarly, some cultures emphasize centralized and vertical management, while others promote horizontal decision making. No culture is superior to others in all situations; moreover, some cultures may be appropriate in certain contexts and become a disability when the organization or the context changes. Organizational governance mechanisms include three dimensions: structure (distribution of functions and coordination); processes (communication, coordination, leadership, learning mechanisms, operational processes and incentives); and the strategic priorities (mission, vision, strategies and action plans). The structure defines the formal authority, functions, responsibilities and lines of communication between the parts of the organization; processes are recurring activities that use the organization’s resources and strategies define the objectives and action plans. In non-profit organizations, strategic axes are essential to align the actions of all members. While organizational cultures change slowly and are largely an emergent property of the evolution of the organization, governance mechanisms can change very quickly by deliberate actions. An important element in the institutional analysis of research organizations is the set of incentives they offer to their scientists because they align individual actions with organizational objectives. It is important to consider the whole set of incentives, both monetary and non-monetary, such as professional development opportunities, as it has been determined that researchers strongly value the possibility of being the first to answer “interesting” questions and be recognized for it. This does not mean that monetary incentives are not important, but they are not the only issue to be considered. The importance of non-monetary incentives is exemplified by the rapid expansion of open- source software production (Friedman 2016). b) Changes in the organization of science The organization of science is being transformed by rapid scientific and technological progress as well as environmental, economic and social changes (e.g., climate change, globalization, migration and demographic and nutritional transitions). The most important changes include:
  • 6. • An increasing share of research is done outside traditional research centers such as US and European universities and institutes. • Disciplines are evolving as new disciplines emerge at their borders and transdisciplinary approaches become more common. • Researchers cannot follow all developments in their own discipline and area of work, forcing them to work increasingly in transdisciplinary networks. • More countries are doing research but it is still heavily concentrated in developed countries and China. • In the stronger research systems the number of tenured positions is shrinking leading to greater employment and financial insecurity. • Public financing of research is shifting from blind grants to time-bound projects, and an increase in research funded by private companies and billionaires. • Technical change in science is accelerating the obsolescence of equipment and professionals. • Increasingly research processes and inputs are protected by patents and other intellectual property rights; therefore, public research organizations need to develop strong capacities in these areas to protect their research outputs and to analyze the implications of collaborating with other actors in research and innovation networks.
  • 7. Contents 1 Introduction................................................................................................................1 2 Conceptual framework for analyzing public research organizations.........................2 3 How is agriculture changing? ....................................................................................5 4 What is an innovation?...............................................................................................7 5 Some features of complex systems important for the analysis of research and innovation............................................................................................................................10 6 Characteristics of research activities........................................................................12 7 The relationship between scientific research and innovation ..................................14 8 Trends in the organization of science ......................................................................16 8.1 How the rapid advance of science affects research organizations...........................16 8.2 New areas of research have emerged, but most resources are invested in a few areas (TICs and life sciences, especially related to human health)...................................17 8.3 Increasing privatization of the cutting-edge research in some areas .......................18 8.4 Public research is more distributed globally but it is concentrated in a few countries; additionally, private research in developed countries is increasingly concentrated in a few companies.......................................................................................................20 8.5 The number of tenured research positions in both the USA and Europe is falling while the number of Ph.Ds. is growing....................................................................20 8.6 Science is increasingly conducted by international networks..................................21 8.7 Financing of time-bound projects is increasing relative to blind grants..................21 9 Changes in extension systems..................................................................................21 10 Framework for the organizational analysis of research organizations.....................24 10.1Organizational capacities for innovation .................................................................26 10.2Organizational learning............................................................................................28 10.3Organizational culture..............................................................................................30 10.4Organizational systems of government....................................................................32 10.5Incentives offered to researchers, including remuneration, working conditions and financing mechanisms..............................................................................................33 11 New approaches for the management of public research ........................................35 11.1Adaptive management .............................................................................................36 11.2The importance of a good monitoring and evaluation system (M&E) ....................37 12 Final Considerations ................................................................................................38 13 Bibliography ............................................................................................................40
  • 8. 1 1 Introduction The socioeconomic environment in which public agricultural research organizations operate is changing at an increasing pace, both in Argentina and the world.1 These changes and new social visions on the role of science in development are imposing new demands on public research organizations. Responding to these demands, Argentina’s Instituto Nacional de Tecnología Agropecuaria (INTA) commissioned the analysis of the most important trends that are currently influencing the organization of research globally and, in particular, in middle income countries. This paper contributes to the debate about the emerging opportunities and challenges faced by these organizations, especially INTA, by presenting a framework for: • Analyzing the most important recent trends that have been documented in the organization of science, especially in developed countries. • Conducting an institutional analysis of public research organizations. • Reviewing new guidelines for the design and implementation of science and innovation policies. Scientific and technical progress and environmental and socioeconomic changes are changing societies and nature faster and in unpredictable ways, forcing organizations to change more frequently (Friedman 2016; McChrystal et al. 2015). But most organizations, both public and private, have serious difficulties adapting (Christensen 2003). To overcome them, they need to develop change strategies and monitor their implementation. The framework presented in this document is based on the characterization of public research organizations as complex entities that evolve in response to external and internal factors. The evolution depends, among other factors, on their organizational capacities and cultures, learning mechanisms (i.e., the creation of new capacities) and governance structure. Public research organizations can be characterized as complex organisms that evolve through the interaction between motivations and capacities. Motivations reflect the opportunities for change and in the case of public research organizations, they include external pressure (reflected in the political discourse and budgetary allocations), scientific progress, and the desire to contribute to social welfare and the advancement of science. Capacities determine how organizations respond to challenges or emerging opportunities. The capacities are built through sustained investments and depend on a number of factors, including the history of the organization, its culture, governance mechanisms, the presence of innovative individuals and the environment in which the organization operates. The influence of each factor changes over time, so that the variables that at a given moment have a positive effect could become negative factors later. A serious handicap of public organizations is the lack of a clear metric to follow whether they are achieving their strategic objectives. In private companies the actual or expected profits serve this purpose. But not-for-profit research organizations that seek to contribute 1 In this paper agricultural research includes all research related to productive activities in rural areas, including forestry, natural resources management and genetic resources.
  • 9. 2 to socioeconomic wellbeing and the sustainable use of natural resources lack simple and relatively easy to measure indicators. These organizations can overcome this problem by (a) clearly defining their strategies to guide their decisions and those of their members, and (b) introducing flexible incentives, processes and effective mechanisms for organizational learning (Hudson 2009). Setting organizational strategies, developing capacities and implementing organizational learning are discussed in detail in sections 2, 0 and 11. The most important external changes that are influencing research organizations can be classified into three categories: • Changes in the agricultural sector and in the management of natural resources (both at the national and global levels), including new techniques and science-intensive products and ICTs. • Changes in the social perception of science. • Changes in the organization of science. The changes in the agricultural sector arise mostly from the integration of output, financial and labor markets (in which migration has a strong impact on both the countries of origin and destination), changes in economic policies, technological change and scientific advances. Section 3 reviews these changes. In 1945, Vannevar Bush, then director of the U.S. Office of Scientific Research and Development published a paper that marked science policy for nearly four decades (Bush 1945). This paper introduced what became known as the linear vision of science, and had three basic principles: (a) basic and theoretical research were the basis of all scientific and technological progress, (b) after a while basic research always benefits society, and (c) science can only be evaluated by scientists. That is, society should finance science abundantly and delegate to the scientific community its own regulation (Kraemer 2006). This view began to change in the 1980s when it seemed that Japan, with a research system much smaller than that of the U.S., grew faster and appeared to be on track to become the largest economy in the world. The analysis of the contrasting experiences of the U.S. and Japan originated the studies on innovation systems (Freeman and Soete 1997), discussed in sections 4, 5 and 7. New models for organizing and controlling science emerged from these studies (Stephan 2012). Currently these trends are accelerating by rapid scientific advances, new mechanisms of interaction between different actors in the innovation systems and new science and technology policies. These changes affect all organizations involved in science, including public and private research organizations, universities, governments, businesses, the researchers themselves and extension organizations; these issues are discussed in sections 8 and 9. Finally, sections 0 and 11 discuss important concepts for the institutional analysis of public research organizations. 2 Conceptual framework for analyzing public research organizations The conceptual framework for the institutional analysis of public research organizations recognizes these organizations as complex organisms that evolve by interaction between motivation and capacities (Ekboir et al. 2009). The motivation reflects the incentives and opportunities to change and include external pressure (from policy makers and budget
  • 10. 3 allocations), science progress, and the desire to contribute to social welfare and the advancement of science. In contrast, for private companies motivations are mostly market or technical opportunities. Finally, for civil society organizations, the greatest motivation is their commitment to a social or environmental cause. Capacities determine how organizations respond to challenges or emerging opportunities. The capacities are built through sustained investments and depend on several factors, including the history of the organization, its culture, governance arrangements, the presence of innovative individuals and the environment in which the organization operates. The influence of each factor on the dynamics of the organization changes over time, so that variables that have a positive effect at a given moment can become negative factors later (Christensen and Raynor 2003). The dynamics of organizational capacities and their interaction with external factors can be explained by the properties of Complex Adaptive Systems, known as CAS. These systems evolve by the interaction among large numbers of actors of different types, conditioned by the history of the process, the socioeconomic environment in which they operate and random events (Crutchfield and Schuster 2003). In the case of public research organizations, the internal actors are mainly researchers and managers; external stakeholders include policy makers, producers, producer organizations, private companies and financial institutions. Organizations implement strategies through two simultaneous processes:  Deliberate strategies are those formally defined by the organization usually through strategic planning exercises.  Emerging strategies are those that result from the accumulation of the decisions of each and all members of the organization, including top and middle managers, sales force, researchers and floor operators. These decisions tend to be tactical and include, for example, the allocation of resources within the divisions or deciding which partners to prioritize. The accumulation of the operational decisions results in the actual allocation of resources within the organization, which may differ from the allocation decided in the deliberate strategy (Mintzberg and Waters 1985). Without effective leadership and learning mechanisms, emerging strategies dominate the evolution of organizations and organizations only react to emerging problems or opportunities. Three of the most important features that influence organizational innovation capacities are the learning mechanisms (because they determine how quickly new information can be generated and used), the organizational culture (because it determines how “unconscious” factors influence innovation activities) and the governance arrangements (because they define the flexibility of the organization to organize their activities and resolve conflicts). Innovation processes are complex because if they could be planned in detail they would be routines, not innovations. The ability of innovators to try and find new things that work better than those in use depends on their ability to integrate into networks that facilitate the sharing of resources and information. In other words, innovations depend on the interaction
  • 11. 4 between individual skills and organizational learning (Dosi, Nelson and Winter 2000). Individual capacities result from the combination of a person’s natural talent and learned skills (Renzulli 2003). On the other hand, organizational capacities result from the interaction between the resources available in the organization (skills of individuals and fixed capital), processes (mechanisms accepted by the organization as the way to do things) and values (including the institutional culture and the long-term goals). Organizational capacities can be built through sustained investments, strong leadership and a proper selection of those responsible for carrying out the activities. It is also essential that the senior management actively foster organizational learning, i.e. (a) help to develop in the entire organization a vision of the changes that must be made, (b) in order to reduce uncertainty, allow an adequate number of pilot projects to identify appropriate changes, and (c) install effective discussions and feedback mechanisms to reach consensus on what is desirable and acceptable in the organization (Davila, Epstein and Shelton 2006; Crutchfield and Schuster 2003; Levinthal 2000). The effectiveness of the feedback mechanisms is essential because the number of options that an organization can try is limited by problems of coordination, availability of resources and organizational skills (Dosi, Nelson and Winter 2000). Learning is also essential in order to take advantage of emerging strategies. However organizations typically have difficulties to learn and innovate because (a) they tend to prefer routines that have so far been successful to innovative approaches that could be positive, and (b) the directors, managers and employees tend to be absorbed by routine activities (Christensen and Raynor 2003; Dosi, Nelson and Winter 2000). Organizational learning begins when the individuals in the organization can better process information to build new frameworks. Individual learning is also a collective process because what an individual learns depends to a large extent on what the other members of the organization know and what the organization allows in terms of experimentation (Bailey and Ford 2003). The organizational learning includes the processes by which knowledge is created and distributed in the organization and is integrated into its operations (Dosi, Nelson and Winter 2000). The organizational culture is a set of assumptions, values and beliefs developed by an organization, mostly in its beginnings and molded by practice over the years. The organizational culture determines to a large extent what is acceptable in the organization (Schein 1991). Some cultures facilitate individual learning while others promote collective learning; similarly, some cultures emphasize centralized and vertical management, while others promote horizontal decision-making mechanisms. No culture is superior to the others in all conditions; even more, some cultures may be appropriate in certain contexts and become a hindrance when the organization changes. The analysis of governance in the organization includes three dimensions: structure (distribution of functions and coordination); processes (communication, coordination, leadership, learning policies and operational processes) and the strategic axis (mission, vision, strategic lines and action plans). The structure defines the formal lines of authority, roles, responsibilities and lines of communication between the parties of the organization;
  • 12. 5 processes are recurring activities that use the resources of the organization and the strategic axis define the objectives and plans of action. In non-profit organizations, the strategic axes are essential to align the actions of all members. While the organizational cultures change slowly and are mostly an emerging property of the organization’s evolution, governance arrangements can change very quickly by deliberate actions (Mintzberg 1999). Individual actors and organizations do not possess all the resources they need to innovate; therefore, they integrate into networks that facilitate the exchange of knowledge, capacities and resources (Powell and Grodal 2005). Networks are organizations with informal structures and a relatively fluid membership. 3 How is agriculture changing? Several publications analyze current changes in the production, marketing and consumption of agricultural products; therefore, this document only discusses the influence of these changes on the organization of agricultural research and extension. Additionally, due to the nature of the study and to the fact that it was commissioned by an Argentine organization, it does not discuss in sufficient detail the situation of the rural poor and small producers in developing countries. Markets for agricultural inputs and outputs are increasingly integrated globally, natural resources are changing (especially climate change and water use) and technical change is accelerating; these trends have strong impacts on farmers. For commercial producers, factors that are not strictly agronomic (such as managerial and commercial issues) are becoming progressively important, forcing them to develop new capacities (this topic is discussed in greater detail in section 10.1). On the other hand, more off-farm employment opportunities, either in urban areas or abroad are opening for households that derive only a relatively small share of their income from agriculture. As the importance of agriculture as a source of income shrinks, these families devote fewer resources to it and prefer to invest in health and education which allows them to better access more profitable markets (Ekboir 2016). Globally farms are converging towards five major production schemes: 2 a) Large-scale production of grains and meats. b) Plantations that produce fresh fruits or inputs for industry such as palm oil, coffee or tea. c) Production of fresh fruits and vegetables which in turn can be differentiated among those who sell to supermarkets and fast food chains on the one hand and those who sell through traditional channels, such as at the farm-gate or in urban wholesale markets, on the other. 2 While these categories can be found all over the world, their relative importance and characteristics vary across countries. For example, small farmers who produce high-value products in the east coast of the U.S. are very different from those of Mexico. Similarly, small-scale traditional producers in Brazil relate differently with large commercial farms than those in Burundi.
  • 13. 6 d) Farmers who sell directly to consumers (for example, in farmers' markets or home delivery). e) Poor rural households that earn a significant portion of their income from off-farm work and have a diversified agricultural production, mainly for home consumption, and eventually sell small surpluses. The first three categories operate increasingly in vertically integrated chains, in which the producers who do not invest in production, management and marketing technologies disappear due to the strong economies of scale that characterize the new production, financing and marketing processes.3 A second feature of these sectors is the increasing “industrialization” of agriculture, in the sense that is progressively dependent on non- agricultural inputs (The Economist 2016; Anlló, Bisang and Campi 2013, Giovannucci et al. 2012, and Committee on Twenty-First Century Systems Agriculture 2010). The firms that control critical inputs become the central actors in the value chains. For example, the “life sciences” companies that supply agrochemicals and transgenic seeds are central actors in the production of grains, whereas in some fruit and vegetable chains the process is increasingly controlled by supermarkets and fast food companies in what has been called the supermarket revolution (Reardon et al. 2009). These companies increasingly determine the technical package that their preferred suppliers have to use, although many aspects of the package are developed by independent firms. For example, supermarkets sometimes offer technical advice to farmers, but the irrigation equipment and greenhouses are developed and sold by independent companies. These trends in society and technology also create new opportunities for small farmers. For example, the number of small farmers in the U.S. is growing strongly to supply the “Locavore movement” - the consumption of locally produced agricultural products - but the majority of these farmers also has non-agricultural sources of income. In Europe there is a strong culture of consuming local foods, a trend that is growing thanks to the development of new communication and marketing technologies. Similar processes are happening in middle-income countries, where small producers use modern methods to supply the high- income urban consumers. For example, farmer markets that sell organic produce are well established in the city of Buenos Aires. Finally, around the world small farmers are becoming differentiated into two groups. Less than 10% has the potential to become commercial producers; the rest is reducing its reliance on agriculture and depending more on other sources of income, especially remittances sent by family members who migrated abroad or to urban areas. These farmers cannot become entrepreneurs and keep their farms to cover their basic food needs and as a retirement insurance. In time, when they are no longer able to farm and their children have moved to urban areas, they will sell the land to more entrepreneurial farmers. More than technical advice, these households need two types support: for younger people, support to develop capacities that help them integrate into high-wage labor markets, and for the older people safety nets in the form of a pension or cash transfers (Ekboir and Rajalahti 2012). In 3 This is not a new phenomenon. In the 1940s Schumpeter (1943) introduced the idea of creative destruction and Cochrane (1958) spoke of the technological treadmill that forced farmers to invest permanently.
  • 14. 7 Argentina these households are probably the largest group of farmers but account for a very small proportion of the national agricultural output and the total agricultural land; in other developing countries they are a large but decreasing share of the population. This last group has received special attention in almost every country. For instance, the 2014 issue of FAO’s flagship publication, the State of Food and Agriculture, dealt with innovation in family farms (FAO 2014). A major problem with the category “family farms” is that it has not been well defined. For example, FAO’s publication mentioned that family farms produced more than 80% of the global food supply; they arrived at this estimate by including in the category family farms from the North American Midwest who farm thousands of hectares with precision agriculture and African farmers planting less than 1 hectare. As the rest of the publication focuses on poor small farmers, it is implied that most of the food is produced globally by poor rural households. 4 What is an innovation? The literature on innovation systems exploded in recent years, but few papers discuss thoroughly what an innovation is and how it differs from the traditional notion of technical change. In this article, an innovation is defined as anything successfully introduced into an economic or social process (Ekboir et al. 2009). This definition has several important implications. First, it emphasizes that an innovation is not only trying new things, but that these ideas or products are used by innovators in processes that include technical, economic and social components (Brynjolfsson and McAfee 2014). This observation highlights the central role of individual and organizational capacities in innovation processes (see Sections 10.1 and 10.2). Second, the definition emphasizes that innovation is not a discrete and finished product. In the traditional framework, farmers passively adopt technical packages without changes, such as improved seeds. This simplistic approach of innovation does not represent the development and diffusion most important innovations as no-till (Ekboir 2003b), mobile telephony, internet commerce and computers (Christensen 2003). Third, the definition implies that innovations are contextual (Bailey and Ford 2003). In the 1990s many small farmers in Ghana began to use a long pole placed at the end of the plot. The pole enabled them to plant the crops in rows facilitating weed control (Ekboir, Boa and Dankyi 2002). On the other hand, advances in biometrics were not innovations for these farmers because they could not use them, in the same way that the stick was not an innovation for mechanized producers. In other words, except for science and art, an innovation does not have to be new for the world or even for the country in which it is developed, but only for the agent that incorporates it. But to use an innovation, the innovator normally has to adjust various elements of his productive and organizational package (Brynjolfsson and McAfee, 2014; Davila, Epstein and Shelton 2006). In the framework of innovation systems, researchers do not generate innovations but scientific information, either codified for example, in a scientific article or embodied in a
  • 15. 8 product, such as an improved seed.4 Researchers also generate tacit information that can only be shared through personal interactions. Scientific information only becomes an innovation when an economic or social agent uses it to improve what she is doing. The innovators use many sources of information, including scientific information, but most of the innovations do not originate in science but in the everyday activities of economic or social agents and in interactions among actors in the innovation system. The innovation system is broader than the research system because in the former participate different types of actors, such as agricultural producers, private companies and government agencies. The innovation system can be strong even if the research system is weak; this happens when research organizations are weak but other actors in the innovation system are strong. Production of vegetables for export in Senegal and melons for export in Central America are examples of strong innovation processes coexisting with weak research organizations. The reverse case has also been observed, i.e. weak innovation systems and strong research organizations, as was the case of the Soviet Union in the 1970s that could send a satellite into space but could not produce a reliable car. In the traditional vision of science, also called the linear vision or the continuum of research and development, all knowledge begins with basic science, expands with applied research and ends with technological development and adoption (Figure 1). Figure 1. The linear vision of science. However, in the study of actual research systems it was found that this stylized picture represents only a few scientific areas such as chemistry (Freeman and Soete 1997). From the perspective of innovation systems, innovations are developed mostly by groups of private actors which may also involve researchers and other actors from the public sector. More than a continuum, innovation processes look like spider webs. In Figure 2, farmers interact with other farmers and with suppliers and buyers to exchange information and resources to experiment and innovate; these interactions are represented by the solid lines. 4 Researchers generate innovations when they develop new methodologies or research instruments that are used in science.
  • 16. 9 As researchers may participate or not in the process, they are linked to the other actors by dotted lines. All these interactions are framed by the socio-economic environment, represented by the blue box. Figure 2. The innovation system. In place of a research motivated only by curiosity, as represented in the linear vision (Figure 1), in the innovation systems framework science is defined by two dimensions: the motivation of the researcher and the consideration of its use (Stokes 1997). Thus, four combinations are defined (Figure 3).
  • 17. 10 Figure 3. New characterization of science. These categories are applied to individual researchers, since researchers in the Einstein and Pasteur quadrant can work simultaneously in the same organization. But each organization has to define what quadrant best defines its vision and mission. 5 Some features of complex systems important for the analysis of research and innovation Complexity theories offer new perspectives for the analysis of research and innovation processes. In particular, these processes can be featured as complex adaptive systems, also known as CAS. CAS are complex because a significant number of actors (including producers, private companies, public organizations, researchers and funding agencies) participate in them, each trying to achieve his/her individual goals and reacting to the actions of the other actors. One of the most important properties of CAS is self-organization (Crutchfield and Schuster 2003; Axlrod and Cohen 1999). Patterns of collective behavior (of groups and of the system as a whole) emerge from the actions of individual actors, the interactions among them and with the environment; these patterns do not exist at the level of individuals. Animals and plants are examples of emerging patterns, since at the most basic level, they are chemical reactions, which organize spontaneously into proteins, cells, tissues, individuals and communities. Animals and plants age, but the chemical reactions that are the basis of life do not. Markets and organizations, are other examples of collective behavior that emerge from the accumulation of the actions and interactions between individuals. Due to randomness and self-organization, CAS are essentially unpredictable and cannot be controlled. Some actors may be more influential than others, but cannot completely determine the system’s evolution. Even more, in a complex system it is not possible to predict the results of an intervention, since the outcomes depend on all the interactions among the parts of the system. For this reason, interventions in the CAS do not seek to “manage” the system, but increase the probability of occurrence of desired events and reduce the likelihood of adverse events, in addition to identifying new questions and tipping points where interventions can have a large impacts (Axlrod and Cohen 1999). The most important instrument to operate in a CAS is the creation of variation combined with an effective selection mechanism. In natural systems the variation is random and the selection is based on reproductive efficiency. This mechanism works because nature does not have temporary restrictions; therefore, it can explore a large number of variations until one that is fitter than the individuals that already exist emerges. In contrast, human interventions change variation and selection in a deliberate manner. An example of this strategy is plant breeding; the effectiveness of this activity depends on having good mechanisms to induce variations and select the best individuals that result from the variations. In the traditional methods, the breeder crosses thousands of lines that he believes may result in offspring with the desired characteristics (he is influencing variation) and then uses selection criteria different from the reproductive efficiency (for example, resistance to a disease) to choose the best individuals. Currently, scientific advances are changing these processes to increase its effectiveness (see section 8).
  • 18. 11 Due to self-organization and adaptation, no optimal solutions exist in CAS; therefore, strategies should seek good enough solutions that can only be found with trial-and-error based on strong learning capacities (Crutchfield and Schuster 2003). An important instrument to operate on a CAS is a monitoring system that identifies the state of the CAS at intervals short enough to enable corrective actions to be implemented if necessary. The literature on management of organizations in CAS differentiate between four types of processes (Patton 2011): 1) Simple: the relationship between causes and effects are stable and clear to all; the causes are separable in the sense that each cause has a direct effect and each effect has perfectly identifiable and separable causes, such as, for example, moving a knob to turn on the light. In such cases, rational design is the best strategy to manage the process, i.e., thoroughly analyze the process and implement the best practices identified from similar processes. 2) Complicated: relationships between causes and effects are not obvious and their understanding requires some analysis by specialists; the process is sufficiently stable that interventions normally result in the desired results. An example is a telephone system that is composed of millions of cables, switches, teams and individuals; however, the probability that a call will be completed in the first attempt is quite high. The strategy recommendation is to analyze the process and use good practices (not necessarily the best). 3) Complex: the understanding of the connections between causes and effects are always tentative because many causes interact among themselves in relationships that change over time. The effects are the result of the entire set of causes so that they cannot be attributed to individual causes or specific interventions. An ecosystem and human relations are examples of CAS. The most appropriate strategy to operate in a CAS is exploration, learning and adaptation. The purpose of interventions is to increase the probability of occurrence of positive events and reduce that of the negative events. 4) Chaotic: The process changes so rapidly that it is not possible to identify patterns; in general these processes are short lived and stabilize in one of the other three types. In these cases, the best strategy is to try to minimize the negative events and conserve resources until the change slows down. An example is an area after a natural disaster where natural, social and economic systems are altered. As they evolve, processes can change of category, for example, when an isolated market integrates into a global market, it moves from simple to complex. Conversely, it can go from complex to complicated, as when a vaccine enables eradication of a disease, eliminating the need of quarantines and collective action for its control. Complexity theories emphasize the need to use adaptive management and organizational learning; which are discussed in sections 10.2 and 11.
  • 19. 12 6 Characteristics of research activities Research has unique characteristics that reduce the effectiveness of traditional management approaches used in other activities. 1. Research has a greater uncertainty than other activities concerning the likelihood of obtaining results, their nature and how long it can take for the results to be used productively. Often research yields outputs that were not expected when the research started, as was the case with many medications. Furthermore, the total value of the outputs may be unknown even at the end of the project; for example, even 60 years after it was first published it is not possible to determine the value of DNA research because it is not possible to foresee all the applications in which it can be used. 2. More than one approach can be used to study a particular problem and it is not possible to tell in advance which one is the most appropriate. Similarly, alternative solutions can exist for the same problem, not all developed through formal research. For instance, losses in the transportation of fresh fruits can be reduced by improving roads, creating new packaging or developing sturdier varieties. 3. In some areas of science, most benefits come from the “best” discovery and not from the total number of discoveries; for example, when several varieties of a crop adapted to a given ecosystem are released, only the best one will be adopted by farmers. In such cases, the research outputs that are not used have no immediate value because it is not a substantial improvement on the best available technology (Huffman and Just 2000). In such cases, research is represented as a race where the winner takes all. 4. Alternatively, often the benefits are distributed among several research approaches; for example, there is no single treatment for cancer (Stephan 2012). This research is represented as a tournament, where the winner takes the largest prize, but there are smaller prizes for many participants. The analogy of the tournament helps to understand the segregation of research organizations according to the talents of their researchers. The best researchers are attracted by the best organizations; the less talented go to organizations of a second or third tier while those at the tail of the distribution leave research or work in laboratories of recognized researchers (Stephan 2012). This process is self-sustained if researchers can move freely; on the other hand, if there is little mobility and researchers have job stability from the beginning of their employment, a researcher’s ability is a random process that is resolved when the organization hires her and on which it has very little control thereafter. As this self-selection process strengthens the initial distribution of research capacities in the national and international research systems, it is very difficult (but possible) to transform a research organization. For this reason, recruitment is among the most important decisions taken by an organization that offers job security. 5. Even research that does not attain the expected results or is not applied in productive processes can be valuable because it provides useful information to guide other research projects or it could be valuable in the future. Even “failed” projects provide information on alternative research strategies, increasing the likelihood of success of new projects. Finally, results that today do not have application, can prove valuable in a different socioeconomic context.
  • 20. 13 6. Scientists have more information than the administrators of the organizations in which they work, because it is very expensive and ineffective to monitor their effort. Given the uncertainty of research projects, the actual effort exerted by scientists cannot be inferred from the results obtained. Researchers who work on more innovative or more risky research have a lower probability of obtaining results than researchers who do more conventional research. But the former can make a greater effort and of higher quality than the latter, although they do not “obtain the results sought”. A system of incentives based on the results obtained discriminates against the most innovative or more risky research, which often yields the greatest benefits in the mid- and long term. This point is of great importance for the design of management systems of research institutions (see section 10.5). 7. Scientific productivity, measured by publications, has a very asymmetrical distribution. One quarter of all scientific papers is published by 2% of the researchers, 50% of the papers is written by 10% of the researchers and the remaining 50% by the other 90% of scientists (Stephan 2012; McClellan and Dorn, 1999). Moreover, bibliometric studies demonstrate that the great majority of scientific papers are never cited, but that does not imply that they are not important because they can influence other researchers through channels not captured by these studies. A system of incentives that prioritizes publications excludes the great majority of researchers. In systems where the compensation depends on publications and recognition, the asymmetric distribution of publications results in an asymmetric distribution of remuneration and of the organizations according to their reputation. 8. Research is increasingly done by national and global networks involving various types of partners and partnership arrangements (Wagner 2008); therefore, public research organizations have to define institutional partnering policies. The need to partner arises because: (a) equipment is costlier and becomes obsolete faster, (b) the growing volume of scientific publications prevents a single person from knowing everything that is published in her area of specialization or the areas she needs for her work, and (c) a large share of scientific knowledge, especially technical, is tacit, i.e. it is not codified in a book or model, which forces researchers to communicate with experienced colleagues. 9. The productivity of a researcher depends on his recognition among peers and that of the organization in which he works. This process is known as the “Matthew Effect” where the best known researchers receive more recognition for their research than less recognized ones who do work of similar quality (Stephan 2012). One of the causes of this effect is the enormous amount of scientific material that is published, which leads researchers to prioritize reading papers written by recognized professionals. The quality of researchers is also influenced by the organization in which they work because (a) its evaluation systems influence the quality of research, (b) the quality of its equipment and facilities affect the work that can be done and (c) the interaction with more active colleagues induces a greater effort. 10. The productivity of the researchers who studied abroad or who developed extensive international networks is higher than that of researchers who only participate in local networks (Gibson and McKenzie 2014; Wagner 2008).
  • 21. 14 11. Even though research is the quest for novelty, disciplines are conservative. It is harder to obtain funds to study new phenomena because the probability of success in newer areas is lower than that of better-known areas. Additionally it is more difficult to publish in the best professional journals results that differ from the consensus of the discipline because reviewers tend to reject ideas that do not fall within the disciplinary paradigms. Finally, the researchers exploring the frontiers of science risk becoming isolated from their communities, which is a problem in an activity that is increasingly a social enterprise. On the other hand, when the most innovative research is successful, it entails great benefits in the form of fame and funds (Von Krogh et al. 2012). 7 The relationship between scientific research and innovation Science has been defined as what scientists do, while technology is what technologists do (Goldfarb 2008; Stoneman 1995). This definition emphasizes that the difference between science and technology does not lie in what the professionals do but on why they do it and what criteria they use for accepting knowledge as true. That is to say, it is recognized that these activities are social processes. Traditionally, scientists’ ultimate goal has been to create new knowledge that was disseminated freely and as quickly as possible through specialized media. In other words, the goal of scientists was the creation of information, a public good.5 In contrast, for technologists, research has been a means to obtain private profits by creating private goods. The race to decode the human genome exemplifies these differences. Two teams of researchers participated in this race; a team was coordinated by a private company and the other by an international consortium of public institutes. The private company wanted to patent knowledge while the public researchers sought to publish their findings as quickly as possible. Both teams researched the same phenomenon (although with different methodologies) and obtained similar knowledge, but one was doing science and the other, technology. The Bayh-Dole act enacted in 1980 allowed private commercialization of results obtained by publicly funded research. This act together with new technologies that spurred the development of commercially valuable science-based products blurred the distinction between science and technology. Before the changes it was generally accepted that public and private researchers should work as separated as possible to avoid “polluting” research agendas with commercial interests. It is now recognized that public-private interactions help researchers to focus their work on relevant issues, as well as identify new problems and understand the requirements of users and market trends. However, partnerships with industry can distract researchers from long-term research and reduce the dissemination of research results in order to protect trade secrets or because the results are not sufficiently innovative (Perkmann and Walsh 2009). Different studies found that the increase in 5 A good is public if it is non-rival and non-excludable. Non-rival means that consumption of the good by an agent does not affect the quantities available to others. Non-excludable means that no agent can be prevented from consuming the good if she wants to. Open television is a public good because the fact that a person watches TV does affect other people who want to watch it (non-rival) and because there is no way to prevent any person with a TV set to watch it (non-excludable). The public nature of a good does not depend on whether it is produced by a public or a private company, but on the two properties just explained.
  • 22. 15 patenting by universities did not restricted most researchers’ access to materials mainly because patents were not respected. However, important cases have been documented in which the private ownership of critical research inputs is restricting their use, for instance, the control of stem cell lines by the University of Wisconsin or genetically modified mice by Dow Chemical (Stephan 2012). Also, there is anecdotal evidence that the exchange of germplasm of the most important crops between public programs from different countries has declined since the enactment by many countries of plant variety protection laws. Similarly, the assessment of a partnership between the University of California and Novartis found that the university administrators and researchers involved in the partnership tended to define public goods as research resulting in marketable products, which changed the nature of public goods (Vanloqueren and Baret 2009). The privatization of research outputs obtained in public universities is having another important effect on science. For many centuries, researchers sought to be recognized as the first to report a discovery; in other words, to own a discovery researchers had to give it away by publishing it, which fostered scientific progress. As research began to have commercial potential and universities sought to exploit it, the incentives for delaying the publication of results strengthened (Stephan 2012). The other difference between science and technology is the criteria to accept knowledge as valid. For science, knowledge is valid when it “explains” the phenomenon under study and the same results are obtained independently by several researchers. In contrast, for a technologist, the criterion is that the knowledge solves a problem; understanding the underlying processes is a secondary objective. These differences are important because they determine in part the incentives offered to researchers. In addition to the differences between science and technology at the level of individual professionals, the literature of innovation systems offers new perspectives on the role of research at the level of organizations and countries. In accordance with the linear vision of science, private companies invest in research less than the social optimum because the research outputs are public goods, i.e. information which once disseminated can be used by anyone for free and without affecting its use by other users. As private actors cannot charge for the use of the scientific information they generate, they do not have incentives to invest and the public sector should cover the deficit, normally in research removed from application such as basic science. In contrast, from the perspective of innovation systems, the public nature of scientific information is not the main cause why companies do not invest in research; the key reason is that to be able to use scientific information organizations have to make large investments for long periods to develop capacities that enable them to understand and absorb it (Cohen and Levinthal 1990). In other words, although the information is free, its use is not and depends on the capacities developed by the organization. From this perspective, scientific information is similar to nitrogen, which is the most abundant gas in the atmosphere but has no economic value unless it is processed in large plants. According to the literature on innovation systems, in addition to addressing the public nature of research outputs, science and innovation policies must foster collaboration between
  • 23. 16 different types of actors, reduce long-term uncertainty and support the development of emerging markets (Lundvall and Borras 2005). Many countries, including Argentina, have implemented important programs to develop technology-based firms and to promote innovation in the agricultural sector. However, anecdotal information suggests that few researchers have changed the way in which they interact with nonacademic actors. The criteria for defining the quality of research have also changed in recent years. According to the linear vision of science, quality was defined solely by peers within the same discipline. In contrast, in the innovation systems framework, quality is also defined by its interdisciplinary nature and relevance to society. The problem is that there are no universally accepted criteria for judging these new dimensions. In the case of evaluation by peers, it is accepted that these have adequate knowledge of the discipline to be able to judge the quality of the work and that this system works in most of the cases.6 But it has been more difficult for researchers to evaluate the quality of inter and transdisciplinary work as individuals are experts only in one part of the research and often do not understand developments close to the disciplinary boundaries. Finally, there are no criteria for assessing the economic and social relevance of research, especially when the evaluators are not scientists (Frodeman 2012). These problems have been recognized in the literature, but no solutions have been found. 8 Trends in the organization of science 8.1 How the rapid advance of science affects research organizations The volume of scientific information is growing so quickly that no researcher can read everything that is published in her area of specialization. Twenty years ago the problem was how to access an updated collection of scientific journals; the problem today is to find relevant information in a sea of useless material. In a recent work, IBM estimated that to follow everything that is published in his specialty, a doctor needs to read 160 hours per week (Brynjolfsson and McAfee, 2014). This phenomenon has seven important consequences. 1. The impact of research depends on its quality and the reputation of the researchers (Stephan 2012; Wagner 2008). New information can be lost in the vast volume of publications unless someone discovers it, and it is easier to be discovered if a famous researcher is one of the authors of the publication. This point is important for the design of systems to evaluate organizations and researchers (see section 11.2). 2. Most of the results of research is “lost” if it is not read by other researchers, regardless of its quality or potential usefulness. To prevent this from happening, organizations and researchers have to develop new capacities to disseminate the results of their research (see section 0). 3. As they do not know everything that is published in their disciplines and in other areas of research which may be relevant, researchers have to collaborate with other 6 Some authors have shown that evaluation by peers is too conservative (Siler, Lee and Bero 2015; Axlrod and Cohen 1999).
  • 24. 17 researchers (see section 8.6). Sometimes these collaborations result in new disciplines that emerge at the boundaries between disciplines, for example, bioinformatics (Weingart 2012; Heinze et al. 2009). Funding agencies of several countries are actively promoting interdisciplinary (between disciplines) and transdisciplinary partnerships (which also include actors who are not scientists). For example, the National Institutes of Health of the U.S. allocated a third of their budget to a program of translational research meant to accelerate the translation of basic research results to improvements in medical and health care (Stephan 2012). 4. Traditional academic departments structured by disciplines are starting to encourage more interdisciplinary approaches, both in research and teaching (ICSU 2011). Some U.S. universities (for example Arizona State University and University of Arizona) have already introduced interdisciplinary curricula where students study by means of case studies and not following structured academic programs. 5. Researchers in areas that change rapidly, such as biotechnology, can become technically obsolete quite quickly. This creates significant challenges for the organizations in which they work because it is necessary to implement programs for continuous learning, either by training the researchers, hiring new professionals or introducing more flexible working mechanisms, for example, organized around specific problems and transdisciplinary collaborations. 6. The number of research outputs produced and marketed is increasing at an increasing pace (Friedman 2016). This has two important consequences. Firstly, research organizations and their researchers have to invest increasing amounts of resources to remain updated. Secondly, the commercial life of many products is becoming shorter due to the emergence of more modern competing products. These features are increase the cost and risk of many research areas, which induces firms to merge. 7. Public research organizations have to maintain the lines of work for several years because research needs time to bear fruit and because researchers have stability in their work. At the same time, due to the rapid progress of science and technology, national research systems and the organizations in them have to adapt faster than in the past. The balance between continuity and change requires a delicate balance because decisions about what to research and how to do it are partially irreversible. This problem is enhanced by the difficulties that the public sector has to end ongoing projects due to the interests that would be affected by the decision. 8.2 New areas of research have emerged, but most resources are invested in a few areas (TICs and life sciences, especially related to human health) Transdisciplinary research is implemented by groups of researchers from different disciplines who interact actively with nonacademic partners, such as farmers, traders and equipment manufacturers. It should be noted that interaction is not equivalent to participatory research; in fact, there are no studies that demonstrate that participatory
  • 25. 18 research is effective.7 Transdisciplinary approaches are becoming more common due to the factors mentioned in the previous sections. This phenomenon, called “Convergence 2” (Roco et al. 2014), began to be recognized as a trend in the interaction between synthetic biology, ICT, artificial intelligence and nanotechnologies; it was later extended to virtually all scientific areas. Other emerging research areas are gene editing and the manipulation of regulatory mechanisms in cells (including CRISPR and RNA interference), cloud computing and Big Data applied to production systems and complexity theories (Axlrod and Cohen 1999). These changes are redefining research, technology and socioeconomic processes in all scientific areas. For example, editing DNA, scientists can block essential processes of specific pests and diseases, creating herbicides or insecticides that only affect the individuals they seek to control. The new areas of research are forcing changes in the more traditional ones, such as plant and animal breeding, where molecular markers, bioinformatics, advanced models of gene expression, remote sensors and editing of genes are combined with traditional plant breeding (Cooper et al. 2014). But the traditional disciplines will still be necessary. For example, biotechnology can introduce new features in seeds, but for farmers to use them, these features must be incorporated into good varieties. Researchers in the new and traditional areas will have to learn how to interact and managers of research organizations must develop the capacity to navigate processes that are more complex from a technical, social, political and legal point of view. 8.3 Increasing privatization of the cutting-edge research in some areas Private investments are increasing in several research areas with important implications for public research, in both developed and developing countries. Not even the most important universities in developed countries can compete with private firms for the best equipment and researchers, which implies that eventually these firms are going to dominate the frontiers of science in certain areas (Stephan 2012; Kraemer 2006). The dominant position that private firms are achieving may influence what is being researched and how it is being done. Increasingly the firms that research areas that interest the agricultural sector, especially biotechnology and breeding, will become akin to the pharmacological firms. This could be an important problem if oligopolistic structures arise and if social costs and benefits differ from the private ones where a few large companies and many start-ups operate side by side. So many research areas are emerging that the big life science companies cannot take advantage of all the new opportunities, which are then taken up by small firms and startups, but the areas less profitable for the private sector such as minor crops and agricultural systems are no being tackled by any of these firms. A major problem of relying exclusively on private research is the difference between different private costs and also between private and social costs. For example, a few 7 An unpublished extensive bibliographic search conducted by the Institutional Learning and Change Initiative of the CGIAR in 2010 did not find any publications that showed the effectiveness of participatory approaches. In fact, all publications assumed that participatory research was the most effective approach. In 2015 IITA conducted another bibliographic search with identical results.
  • 26. 19 processing companies hired farmers in a valley of the state of Jalisco, Mexico, who produced tomatoes as monoculture. After some years, the crop was attacked by a virus that survived in the soil, and therefore tomato production in the area had to be discontinued. The processing companies moved production to another valley, but the producers in the affected area lost an important source of income. The problem could have been avoided with a more holistic production approach supported by transdisciplinary research involving public and private researchers working with producers. As no organization can cover all areas of research, from basic research to product development, private companies collaborate with public and private organizations that possess capacities they lack. The nature of the organizational capacities is discussed in more detail in the section 0. Two non-traditional capacities that public research organizations will need strengthen are intellectual property protection and communications. The protection of intellectual property is becoming increasingly complex. In fact, privatization of research is generating what has been called the tragedy of the anti-commons: the excessive fragmentation of intellectual property rights may slow research and development of products because each patent owner can block other researchers and because it increases the cost of understanding what patents may be affected by the research (Dosi, Llerena and Sylos Labini 2006). In such an environment, how can public research organizations access all the inputs they need? Who is going to pay the increasing indirect costs of research? The value of intellectual property protection is being affected by the rapid advancement of science and technology. A patent creates a monopoly for a specific period but the period can be shortened if the protected product is rendered obsolete by a more advance competitor; in such cases, the patent loses its value before its expiration. The high cost of patenting and enforcing the patent favors large companies with plenty of resources to protect their intellectual property. Another issue that can arise from the privatization of research is the possible emergence of oligopolies in input and output markets. In such cases, the state should regulate those markets, which requires that the regulators develop strong analytical capacities. However, many developing countries do not have these capacities. Even in developed countries many regulatory agencies cannot follow scientific advances, so they request the advice of public researchers. In order to be able to provide this service, public research organizations need to transform their systems of incentives and evaluation, valuing other activities in addition to scientific publications (see section 11.2). A related issue are the conflicts of interest that may arise if public research organizations play both roles of partners of private firms that fund their research and advisers of regulatory bodies. In order to avoid this problem it is necessary to strengthen the technical capacities of regulatory bodies so that they depend less on public researchers. Communication specialists will become more important because the social perception of the benefits and problems that emanate from scientific research has become a driving force of the directions that science can take. Even more, it is expected that the influence of communications will continue to grow (Rodhes and Sawyer 2015). Communicators should
  • 27. 20 help to educate non-scientists in the advantages and limitations of scientific research and to develop effective communication strategies. Due to the privatization of research, many science-intensive products are distributed simultaneously across the world, which allows developing countries to access technologies developed in developed countries without delay (Archibugi and Michie 1998). This reduces the need for private investments in developing countries in certain areas of research, for example, ICT and new herbicides. But this strategy is valid only for research outputs that can be used in different geographies with little adaptation, which is unusual in agriculture and management of natural resources. 8.4 Public research is more distributed globally but it is concentrated in a few countries; additionally, private research in developed countries is increasingly concentrated in a few companies Overall, research is increasingly concentrated in developed countries and China. The scientific dominance of the U.S. until the 1990s is being challenged by the European Union, Canada, Australia and China, followed far behind by a few developing countries such as India and Brazil (Stephan 2012; Wagner 2008).8 But as the majority of the new scientific powers have institutional environments and cultures less favorable for the transformation of scientific outputs in innovations, it is expected that the U.S. will continue to dominate innovation in the coming decades. 8.5 The number of tenured research positions in both the USA and Europe is falling while the number of Ph.Ds. is growing The number of tenured research positions is falling throughout the world, while the number of researchers with non-tenured jobs is growing. Even more, increasingly the work and sometimes the salaries of tenured researchers is funded through time-bounded projects (National Research Council 2014(b); Teitelbaum 2014). The reliance on financing of projects makes researchers more conservative and less prone to explore lesser known scientific areas (Stephan 2012). This trend is particularly important for research on complex processes, such as adaptation to climate change or agricultural systems. In addition, these financial procedures reduce the incentives that researchers from advanced research institutions have to collaborate with organizations from developing countries in long-term projects that do not pay for their salaries. Finally, a growing number of Ph.Ds. accept postdoctoral positions (sometimes for several years during which they work long hours for low salaries) with the hope of eventually achieving a tenured research position. But as the number of these positions is falling, many postdocs end up in non-academic jobs (Stephan, 2012; Lieff Benderly 2014; Teitelbaum 2014). The imbalance between supply and demand of scientists is an opportunity for 8 The funding of research in Brazil has been severely reduced in the current crisis.
  • 28. 21 developing countries willing to invest in their research systems. But this requires long-term commitments, major investments and appropriate incentives.9 8.6 Science is increasingly conducted by international networks Researchers from different organizations and countries are increasingly collaborating with each other (Wagner 2008). Networks help researchers to exchange tacit knowledge and access resources, especially specialized equipment (van Rijnsoever et al. 2008; Kraemer 2006). But the majority of researchers from developing countries has few resources to participate in international networks, for example, taking part in professional meetings or visit advanced research organizations (Wagner 2008). Another way to participate in international networks is to invite foreign researchers and facilitate access to local research projects and contacts with local researchers and policy makers. As networks grow, reputation becomes critical to attract resources and collaborations but researchers from developing countries often do not have enough resources to increase their visibility in professional fields (Wagner 2008). 8.7 Financing of time-bound projects is increasing relative to blind grants Thirty years ago public research was financed mostly through blind grants (non-conditioned direct transfers). Over time, these grants were replaced by financing of time-bound, well- defined projects (Kraemer 2006). Studies of research financing found that, as a result of this change, (i) the volatility of funding has increased, (ii) discontinuities in political processes conspired against long-term research; (iii) it has been more difficult to develop strategic partnerships; (iv) researchers spent more time searching for funds and preparing reports, which reduced the time available for research (Teitelbaum 2014; Stephan 2012; Heinze et al. 2009; Vera-Cruz et al. 2008; Wagner 2008). In addition, as reputation matters for fund raising, funding is increasingly concentrated in the more established researchers, forcing young researchers and those from developing countries to depend on senior researchers. Stephan (2012) reports that researchers funded with long-term blind grants are more productive and creative than researchers funded through projects, provided that quality controls are enforced. 9 Changes in extension systems The role of extension changed over time and geographies. Countries with dynamic agricultural markets established extension systems more than a century ago but in recent 9 The magnitude of the imbalance between the supply and demand of highly trained scientists, particularly in the U.S., is a highly politicized issue because it covers immigration policies and support for firms in science- intensive areas such as biotechnology and information technology. As a postdoctoral position is considered academic training and not a stable job, postdocs receive lower salaries than more established researchers who do the same work. The associations of postdocs and some researchers accused universities and funding agencies of using postdocs as cheap labor instead of recruiting established researchers with more job security and higher salaries. Recently, the U.S. government ruled that postdocs are workers (and not trainees) and therefore are entitled to overtime compensation. Although it is expected that this ruling will have important impacts on research activities, there is no consensus on what they will be.
  • 29. 22 years the changes in agricultural systems and in the organization of science changed the demands on extension, which generated a crisis that has not yet been resolved. Since its inception in the nineteenth century in the USA and Europe, the mandate of public extension organizations was to help farmers to adopt more productive technologies developed in public universities. Even more, as many of the research carried out in the universities still required significant adaptation before it could be used by farmers, a network of state experimental stations was created in the USA. As it was also accepted that researchers could not research and interact with a large number of farmers, in 1914 cooperative extension systems were created at the national, state and county levels. The second half of the XX century was characterized by a steady expansion of farm sizes and the privatization of the production of some inputs, like seeds. As a result, the demand for public extension by commercial farmers producing traditional products (mostly grains, milk and meat) shrank. In 1972 two reports strongly criticized public research and extension systems. The Hightower report argued that these systems were too focused on agricultural firms and not sufficiently on family farms. The Pound report complained that public universities were not doing enough basic research, that their science was not good and that they did not contribute to the future of agriculture. The conclusion of the Pound report was that researchers were too “near” extension agents and did too much applied research, adapted to local conditions. In the following years most agricultural universities expanded research in biotechnology and other cutting-edge areas and reduced the more traditional research in agricultural products and production systems. This shift was reflected in the reduction of the agricultural departments and the dismantling of the majority of the plant breeding programs. Extension organizations have changed in response to changes in the sources of information used by farmers. Today most commercial farmers rely increasingly on private advisers, often linked to the sale of traditional inputs, e.g., fertilizers, and more recently to the purchase of specialized services (such as the analysis of data in precision agriculture), direct communications with researchers and recommendations from companies. At the same time, government support for extension weakened, due to the decrease in the number of farmers and rural laborers (i.e., they became a smaller political constituency) and the limited support extension agents could provide to “non-traditional” small farmers that supply more sophisticated local markets, for example, organic producers (Buttel 1991). The new environment resulted in a sharp decrease in the number of extension agents. Today extension in public universities in the U.S. interact mostly with professional who advise commercial farms, and occupy niches that private advisers do not occupy, for example, disseminating information on the management of natural resources. But the future role of public extension is being questioned and the state extension services are still trying to define their niche (Al-Kaisi et al. 2015). In the Netherlands public extension evolved from disseminating technical information generated in Dutch universities to help farmers to search for scientific and technical information and foster farmer organizations. At the end of the 1980s extension was privatized in the sense that extension agents lost their status as public employees (Eicher 2006). Extension in Norway followed similar paths (Eicher 2006). Since the 1990s,
  • 30. 23 following research on innovation systems, several European organizations began to foster interactions between different actors in the agricultural and industrial innovations systems (Klerkx and Leeuwis 2008). Public extension in developed countries faces six major problems: a) Its traditional customer base (large commercial and “progressive” farmers) is shrinking because farmers are becoming more professional, information is more easily available, and these farmers prefer to consult with highly specialized private advisers or directly with researchers (Buttel 1991). b) The public extension systems were designed to disseminate public and non-proprietary information generated in public research institutions but an increasing amount of information is private and patented (Buttel 1991). c) As farmers integrated into markets for inputs and outputs, the economic and financial dimensions of agricultural production increased, but public extension agents usually do not have the required training to advise on these issues. d) New actors such as public regulatory agencies require help to access and understand scientific information because they have less information than the firms they have to regulate; however, extension agents usually are not prepared to work with regulators, especially at the state and local level. e) Farmers need impartial technical information to be able to compare commercial inputs. To generate this information, research organizations have to conduct unbiased experiments; similarly, extension agents have to be impartial when disseminating information, which requires that the research and extension systems do not have conflicts of interest. This problem is similar to a sick person who has to decide what medicine to buy; it is assumed that doctors provide an unbiased service, but it is not always so since many cases of doctors having conflicts of interests have been documented. f) The Big Data revolution is revolutionizing farm management; it has been difficult for public extension agents to follow these developments which are mostly done by private firms. Another challenge for public extension services in several countries, such as France, Brazil, Bolivia, Paraguay and Argentina, has been the emergence of farmer organizations that validate and disseminate technical information. These organizations are managed and financed by farmers. Often the interactions between public extension systems, private firms and producer organizations have been weak. Important changes in agricultural extension organizations have been introduced in developing countries since the beginning of the XXI century. Following the limited impact of the traditional extension models, most developing countries closed their public extension services, which were replaced by a variety of arrangements (Christoplos 2010). At the same time and following the increasing acceptance of the innovation systems framework, governments and donors recognized that small farmers needed not only technical advice but also support in a variety of issues such as education, health and access to markets. Thus,
  • 31. 24 rural advisory services became the new paradigm for extension, which involved more activities than just transfer of technologies. Despite the many arrangements tried in many countries, no cases have been documented in which public advisory services have played a major role as development agents. Instead, this role has been played by local and international NGOs (McChrystal et al. 2015; Deaton 2013), each one specialized on specific topics, such as education, health and access to markets. The most important drivers of this trend are:  There are many types of family farms with many types of needs; no organization can cover all these topics.  As agriculture is losing weigh in the livelihood strategies of poor rural households, adoption of more productive technologies is not among their main priorities (Ekboir 2016); in other words, agricultural technology transfer is not the main priority in the new packages of rural advisory services.  Most professionals in the public extension services do not have the professional and non-professional background to advise on the new priority areas.  Public extension agencies are still experiencing the same problems they faced years ago, i.e., insufficient budgets, lack or resources, low salaries and lack of training, compounded by more than 20 years of institutional decay.  After the failed experiences of the 1980s and 1990s, most donors lost confidence in public extension agencies and they prefer to finance NGOs which are more flexible than public organizations and respond faster to donors’ priorities.  Market led development projects, i.e., projects that seek to integrate small farmers to markets, have not been very effective, because it has been estimated that less than 10% of these farmers have been able to continue their commercial activities after the external support ended (Ekboir y Rajalahti 2012). Even more, not even the CGIAR centers have been able to maintain their relevance in support to small farmers, even though they pioneered many of the methodologies to work with these farmers that NGOs adopted later. The reason why the international centers could not keep their central role is that they defined themselves as global research actors which were not supposed to work at the national or local level; once the NGOs started to develop capacities to manage this type of projects, they were sought by the donors and displaced other actors in development projects. 10 Framework for the organizational analysis of research organizations The analysis of organizations followed advances in scientific theories, especially systems analysis in the 1960s and theories of complexity in the 1990s (see section 5). The mechanistic understanding of organizations, based on systems analysis, described organizations as deterministic mechanisms that optimize their operations subject to restrictions that they cannot change. The organizations and the environment in which they operate are seen as predictable and simple and therefore management should be based on thorough detailed planning supported by “scientific” studies. When implementation departs
  • 32. 25 from what was planned, the organization should invest more resources to correct the deviations. This management model is known as “command and control”, or in its more modern version, results based management (McChrystal et al. 2015). The alternative vision, also called the vision based on resources, represents organizations as complex adaptive systems and uses metaphors of the biological sciences, in which organizations resemble living beings that respond to the signals they receive from a changing environment. i.e., the paradigm of efficiency is replaced by that of adaptability (McChrystal et al. 2015). Although efficiency continues to be important, adaptability becomes the most important criterion. In this framework organizations pursue their goals using their idiosyncratic resources and the capacities they developed. The organizational capacities arise from the interplay between resources (individuals and fixed capital), processes (the formal and informal mechanisms through which the organization manages its activities) and values (including priorities, the institutional culture and long-term goals). In new organizations most of the capacities reside in the resources, especially its people. The hiring or departure of a key person can have great influence on the organization. With time, successful organizations transfer their capacities to processes and values (Christensen and Raynor 2003). Internally, adaptive organizations invest time and effort to (a) ensure that their members internalize the mission, vision and organizational culture; (b) develop channels of communication among its members, and (c) establish a more horizontal hierarchy where each member has some autonomy to make decisions. Externally, these organizations form networks with other actors. This management model has been adopted by the most innovative companies in the world (for example, Google, Facebook, Whirlpool and GE) as well as the U.S. armed forces in the Middle East (McChrystal et al. 2015). Another important shift in the theory of the organizations was the recognition that organizations of economic and social importance include not only private companies but also public, non-governmental, private non-profit, community-based organizations in addition to different types of networks. Each type of organization has a specific structure that results from its objectives, cultures and histories; no structure is inherently better than the alternatives. In the new framework, an organization is defined as a group of actors who collaborate for a sustained period of time (Ekboir and Rajalahti 2012). The actors can be individuals, firms or any other type of structure; organizations can be formal, as a company, or informal, such as a network or community of practice. In a similar way, an organization can be defined as a coalition whose members and stakeholders seek to maximize their individual interests (van Rijnsoever et al. 2008). From the sustained collaboration, the members of the organization develop a common culture, communication codes, incentives, routines and governance arrangements. These elements define to a certain extent what the organization does and how it does it. Five special features of public research organizations are that:
  • 33. 26  There are no clear measures of success; while private companies seek profits, non-profit organizations’ objectives include social welfare and economic development which are harder to measure (Hudson, 2009; Fukuyama 2005);  Usually, research outputs are obtained after several years and outcomes become visible long after the research ended (Ekboir 2003a);  It is difficult for research organizations to change their lines of research due to public sector regulations, especially the management of human resources;  If stakeholders and the legislative and executive branches cannot define together what they expect the research organization to do, its objectives and mandate emerge from complex interactions between many actors from different sectors (Fukuyama, 2005);  Usually it is not possible to establish a simple and direct connection between research activities and their impacts (Ekboir 2003a). Due to these features, it is necessary to define new approaches to manage research, especially the incentives and monitoring and evaluation systems (Mayne and Stern 2013; Hudson 2009). 10.1 Organizational capacities for innovation A major shift in the analysis of organizations has been the recognition of the importance of organizational capacities for innovation, i.e. the skills to integrate internal and external resources to address emerging opportunities or problems (Ekboir and Rajalahti 2012). Innovation capacities depend on both individual (for example, creativity) as collective attributes, including routines, collective learning mechanisms and organizational cultures. Innovation capacities cannot be bought but have to be developed through strong leadership and sustained investments. But it has been difficult for public organizations to sustain such programs because its management changes relatively often, they struggle to keep their professionals up-to-date and have restrictions for committing to multi-annual budgets that depend on political decisions. This does not mean that these organizations cannot implement capacity development plans but in order to do so, they have to define long-term master plans divided into independent modules. In this way, it is possible to implement activities limited in scope that contribute to the long-term objective. Organizations cannot develop their capacities arbitrarily and must follow certain paths; therefore, the organization’s history not only defines the current options but also sets limits to the options that will open in the future. In other words, the long-term capacity development plans are largely irreversible (Teece, Pisano and Shuen 2000). It is useful to distinguish between technological and organizational capacities, although the two overlap in the real world. The former relate to scientific and technological knowledge, and routines to manipulate nature (for example how to transform a piece of metal into a particular structure). The organizational capacities, in contrast, are shared elements of knowledge and routines concerning governance, coordination and social interaction within the organization and with external entities (e.g., suppliers or customers). The organizational capacities are fundamental in the definition and implementation of the so-called “business
  • 34. 27 model”, i.e. the mechanisms by which an organization seeks to achieve its objectives. All organizations, even non-profits have a business model, although it may not be explicit. The organizational capacities reflect the collective capacity to perform certain tasks. These capacities have several components, including routines, the ability to learn (including the capacity to change routines) and the organizational culture (Dosi, Nelson and Winter 2000). In addition to being an essential part of the institutional memory, routines reflect governance structures, compromises among divergent interests, internal control methods and relatively decentralized decision-making mechanisms. Essentially, routines are conscious or unconscious agreements emerging from conflicts of interests within the organization (Coriat 2000). Sometimes routines are so complex and tacit that the organization itself is not aware of their existence or they do not understand them; for this reason, it is very difficult to change existing routines without decisive interventions by managers to align the organization on the change process. Capacities are specific to each organization – they are developed from investments and idiosyncratic processes. Because it is difficult for other organizations to copy or buy them, some organizations succeed where others fail. For example, the Xerox laboratory in Palo Alto developed in 1973 a personal computer with a graphical interface and mouse (copied by Apple), an operating system that could use several applications simultaneously (sold to Bill Gates who used it as the basis for Windows), Ethernet connection and the first WYSIWYG word processor, i.e., a word processor that showed on the screen how the document would look when printed (Carayannis, Gonzalez and Wetter 2003). But Xerox’s culture, focused on photocopies and large computers did not allow it to see the potential of these discoveries and to take advantage of them. Capacities are contextual; capacities that confer advantages in a given context can become disabilities when the environment in which the organization operates changes (Christensen 2003). The allocation of resources is one of the most important processes in any organization and influences the creation of capacities. In a complex organization there are two mechanisms for allocating resources. The deliberate allocation is the one decided by the organization in its planning process. In contrast, the emergent allocation results from the accumulation of all the decisions made continuously by all members of the organizations from the top management to the lowest employee. Less hierarchical employees decide which actions prioritize (for example, which farmers to visit), the mid-level managers decide which options they will present to their supervisors (removing the options that they believe the supervisors will not value), and the top executives decide the overall strategy, partly in response to the feedback they receive from the employees (Christensen and Raynor 2003). The combination of these processes determine the real strategies because they result in the actual allocation of resources. One of the most important responsibilities of the organization’s senior management is the identification of the emerging strategies and compare them with the deliberate strategies. The differences indicate problems or opportunities that the organization could address.