Paper submitted for publication in “Science and Public Policy”
STS and STP divide: Are there ways to build bridges between the two?1
Monica Salazar2, PhD candidate, School of Communication and Centre for Policy
Research on Science and Technology –CPROST, Simon Fraser University,
Vancouver, Canada
Abstract
There is no simple way to map the field of science and technology studies. Some
authors argue there is a divide between social and policy studies of science and
technology. This paper will first explore the origins of STS studies and STP
studies, and the principal schools of thought within each area in relation to
technology, followed by an explanation of why there is a divorce between the
two; and concluding with some proposals on how to connect the two areas,
combining the principles of constructive technology assessment and the system
of innovation approach. The final objective of the article will be to highlight
common areas of interest of STS and STP, looking for paths to follow on that
integration.
Introduction
There is no simple way to map the field of science and technology studies. One
could identify different points of entry. For instance, there is a disciplinary
approach (e.g. sociology, history, philosophy, and economics). There are also
distinctive bodies of literature studying the various phases of technological
development (e.g. design, construction, diffusion, impact, perception). A different
focus is looking at the role that technology plays in society (i.e. shaping, shaped
by, or co-evolving). Finally, we can divide the field in three major areas:
qualitative studies (usually under the STS label), quantitative studies (i.e.
scientometrics), and policy-oriented studies (what I named STP). For the purpose
of this paper I will focus on the latter categorization, and especially on the
relationships (divide) between STS and STP studies.
Some authors (i.e. Spiegel-Rosing, 1977; Teich, 2001) argue there is a divide
between social and policy studies of science and technology. While others (i.e.
Williams & Edge, 1996) affirm that some streams of science, technology and
society (STS) studies (especially social shaping of technology), have been
concerned with technology policy. It can be argued that these academic
communities are quite differentiated, with very little overlap. Nonetheless, that
2
does not mean that STS scholars have not influenced policy design, however,
that is not their main concern; while science and technology policy (STP)
researchers do seek to affect policy directly.
STS started as a movement, with a critical posture of scientific and technological
development and its impact upon society, proposing alternatives to control S&T.
STS later turned into an academic field, more interested in knowledge creation
with a strong disciplinary focus (e.g. sociological, philosophical, or historical).
STP, according to some authors, was grown out of the ‘control’ sphere of STS.
This paper will first explore the origins of STS studies and STP studies, and the
principal schools of thought within each area in relation to technology; followed
by an explanation of why I considered there is a divorce between the two; and
concluding with some proposals on how to connect the two areas. The final
objective of the article will be to highlight common areas of interest of STS and
STP, looking for paths to follow on that integration.
1. STS studies: emphasis on technology
STS is a contested acronym: some understands it as ‘science and technology
studies’, while other see it as ‘science, technology and society’, ‘science and
technology in society’, or ‘social studies of science and technology’. For this
paper STS stands for “Science, Technology and Society” emphasizing the
societal aspects of scientific and technological development. Concerns about
S&T were born of World War II, when people recognized the complex and
problematic - sometimes undesirable - relationships between power and science.
STS emerged clearly in the late 1960s as a social movement, along with other
social upheavals that appeared then (e.g. environmental and feminist groups).
Because of its origins, STS studies have been critical of S&T developments and
often try to propose ways to control S&T. Later on, in the 1980s, STS was
reinvented and turned into an academic field, mainly focused on knowledge
creation, somewhat neglecting policy and control issues. For the purposes of this
article, I will be focused on STS approaches in the area of technology.
Arie Rip affirms that STS is an eclectic field of study, encompassing multiple
disciplines (e.g. history, philosophy, sociology, political science, economics,
innovation and management studies, psychology, literary and textual analysis,
cultural studies, and anthropology) under one research interest: science and
technology and their roles in society. He furthers argues that “a certain looseness
of method may well go with such an open-ended approach” (Rip, 1994).
Williams and Edge (1996) in their review of social shaping of technology include
in their analysis the economics of technological change and its contributions to
STS studies. Nevertheless, they state that few scholars within economics of
technical change, would recognize themselves as part of any STS school.
3
Usually, economics and innovation and management studies are not part of STS;
I will follow that tradition.
Traditionally, STS scholars have had strong roots on their mother disciplines,
being the most influential: history, philosophy and sociology. Although these ties
have weaken over the time, the disciplinary divide is still there; interdisciplinarity
is still to be achieved. Susan Cozzens affirms that “while disciplines are still very
much in evidence in this research community, there is no one-to-one
correspondence between the topics studied and the traditional disciplines.
Nonetheless, the disciplines continue to play a strong role in STS. Most people
who study S&T still do so within a single discipline” (Cozzens, 2001: 57).
The debate on STS has been animated by two opposite views on technology:
one that affirms that technology shapes society, and the other that society
shapes technology. The former, is commonly associated with the notion of
technological determinism; while the latter could be labeled ‘social shaping of
technology’, which covers various approaches, such as social constructivism and
actor-network theory. In a metaphorical way, one looks at the forest and the other
at the trees. Neither provides an overall view; both have failed to give us a
comprehensive view of technological change and the major forces driving social
change.
1.1. Technological determinist perspectives
There are many views on technological determinism and how to classify it; the
most common labels being hard and soft determinism. According to Marx and
Smith (1994), on the one hand, hard technological determinists impute agency
(the power to produce change) to technology, and imply that technological
development is inescapable and inevitable. On the other hand, soft determinists
recognize human agency, and that changes in history are due to various and
complex social, economic and cultural factors. However, “agency, as conceived
by soft technological determinist, is deeply embedded in the larger social
structure and culture –so deeply, indeed, as to divest technology of its presumed
power as an independent agent initiating change” (Marx & Smith, 1994: xiv).
To organize the various arguments and view, it is useful to use Bijker’s proposal
to differentiate between technological determinism as a theory of technology or
as a theory of society. He wrote that:
“Technological determinism comprises two ideas: technological
development is autonomous, and societal development is determined by
technology. […] It seems wise for analytical purposes to reserve the term
technological determinism only for the second idea, a theory of society,
[keeping it] separate from the idea of an autonomous technology, a theory
of technology” (Bijker, 1995: 238).
4
Bimber distinguishes three different versions of technological determinism:

Normative account: based in Habermas3, suggests that technology can be
considered autonomous and deterministic when the norms by which it is
advanced are removed from political and ethical discourse. Jacques Ellul 4
is a good exponent of this version.

Unintended consequences: derives from observations of the uncertainty
and uncontrollability of the results of technological development. Langdon
Winner is an advocate of this thesis.

Nomological account: “implicit in this account are two claims: that
technological development occur according to some naturally given logic,
which is not culturally or socially determined, and that these developments
force social adaptation and changes” (Bimber, 1994: 84). Robert
Heilbroner is considered a champion of this account, but just when making
reference to his 1967 article.
Bimber maintains that the only truly determinist account is nomological. He
makes a good point explaining what the concept should stand for:
“Technological determinism should hold that history is determined by laws
or by physical and biological conditions rather than by human will; this
makes it deterministic. … Technological determinism should be truly
technological in meaning. That is, technology should play a necessary part
in the way that preceding events or states of the world determine the
future. ... Technology is the medium through which physical laws, some of
which we can learn through science, shape the course of human events”
(Bimber, 1994: 87).
Andrew Feenberg says that technological determinism is based on two theses:

“The pattern of technological progress is fixed, moving along one and the
same track in all societies.

Social organization must adapt to technical progress at each stage of
development according to ‘imperative’ requirements of technology”
(Feenberg, 2002: 138-139).
There are some similarities between these approaches: societies can be ordered
along a simple and unique continuum, and culture does not play a role in shaping
technology. What is meant by the term “technology” is very important. To fit
technological determinist approaches, technology has to be understood as an
artefact. When a broader definition is used, encompassing knowledge about
artefacts and process and systems of organization and control, it becomes
problematic.
5
Heilbroner (1967, reprinted 1994) has an interesting intake on technological
determinism. He assets that “the technology of a society imposes a determinate
pattern of social relations on that society” (Heilbroner, 1994: 59). The question he
was attracted to, was whether technology determines socioeconomic order: Did
medieval technology brought feudalism and was industrial technology was the
necessary condition for capitalism to emerge. In his words, [this] “places
technological change in the position of prime mover of social history”. Then he
asks: “Can we then explain the ‘laws of motion’ of technology itself? Can we
explain why technology evolves in the sequence it does?” (Heilbroner, 1994: 5455). To answer the first question he develops the idea of path dependence
(something not explored at that time, neither empirically nor theoretically). He
says that there is a ‘fixed’ sequence to technological development, and works on
three ideas to explain it: the simultaneity of invention (technological clustering);
the absence of technological leaps (technological advances appear essentially
incremental, evolutionary); and the predictability of technology. On reading
Heilbroner today, he looks more like an evolutionist5 rather than a determinist.
To refute technological determinism positions, we have to escape the trivial level
of observation that technology is man-made, and hence subject to many societal
influences. Many studies have shown that culture plays a role in shaping the
history of technological development, and societies do not develop along a
unique, fixed path. What is interesting to explore is if technology determines
social change or not, if it is a major (or ‘the’) driving force behind major societal
changes, and if social organizations must adapt to technical progress. In this
sense, Cutcliffe notes that “the obduracy of technology must be recognized in
terms of its ability to form enduring practices, theories and social institutions, with
the very real resulting ability to then determine social development” (Cutcliffe,
2000: 51). Whether we like it or not, a chosen technology will affect how people
live, work, have pleasure, etc. However, this do not means that we cannot
change the direction of technological development and ‘control’ the possible
impacts that specific technologies have upon society.
In some way, it can be argue that ’technological determinism’ is a poor choice of
words to explain certain phenomena, that is: technology has been the most
important force driving social change in the last 20 to 30 years. Unfortunate, the
concept has a negative connotation, and as Bimber notes “the term is used in a
muddy and imprecise way” (Bimber, 1994: 81). This does not mean that social,
cultural, political and economic factors do not shape technology. It is not that
technology shapes itself; however, technologies complement each other, or
depend upon one another, especially when we are dealing with general purpose
technologies, which are highly pervasive throughout the whole economy and
society. This leads us to explore technological determinism as a theory of
technology.
6
The idea of technological determinism as a theory of technology implies that
technologies are autonomous, they develop at their own pace without human
intervention, they have quasi magical powers, and they acquire a life of its own
once introduced into society. Simply put, this thesis gives agency to technology
(Marx & Smith, 1994). This is hardly sustainable from any perspective, and it
really does not explain how technologies develop. It is based on the
consequences rather than the genesis of technological innovations. As a theory
of technology, technological determinism does not seem very useful or sound in
explaining how technical change occurs.
It is easy to fall into ‘quasi’ technological determinist stand-points when looking at
major changes suffered by a society or a group of nations during long periods of
time. These type of studies provide a big picture, an economic historical
perspective, overlooking the ‘people’ behind the creation of specific technologies.
In these cases, it is common to encounter terms such as technological
trajectories, techno-economic paradigms6, technological regimes7, and
technology systems, which give the erroneous idea of being technologically
deterministic. What they mean is that technologies build one upon the other, they
form ‘clusters’ of technologies, and each regime or paradigm characterizes an
economic era, dominated by one or more ‘general purpose technologies’ (GPT)
or enabling technologies. These are technologies that are employed throughout
the economy and the society, they help to produce a broad range of products
and are used in a wide array of processes and services (e.g. information and
communication technologies). The replacement of an all pervasive GPT alters
the paradigm. These analyses, based on economic and historical accounts, are
much better at explaining how technologies develop and produce changes in
societies, and as such, they are more adequate and robust theories of
technology.
Besides this phenomenon, it is important to acknowledge that when a new
technological paradigm emerges, it is accompanied by structural changes and
the co-evolution of economic and social movements, with all these changes
following a pattern, the cyclical nature of capitalist development (i.e ‘Kondratiev’
waves)8. Freeman and Louca (2001) affirm that evolution of societies follow
some recognizable patterns, which depend in the relationships between five
semi-autonomous subsystems: science, technology, economy, politics, and
general culture. They do not give precedence to any of them, despite recognizing
that at particular times a subsystem could be the primary driving force of change
or have a predominant influence.
Why these approaches are not technological determinist, even if they
acknowledge the great importance of technological change as a determinant of
long-term economic growth? Let me quote Lipsey, Carlaw and Bekar 9, whom
present three propositions to challenge the classification of being technological
determinists:
7
“First, major new technologies, particularly transforming GPTs, have
important effects on the socio-economic system of any country into which
they are introduced. Second, the same technology introduced into
different places, and/or different times, will have different effects because
the rest of the political, social, economic and institutional structures will
differ between the two situations. Third, because knowledge builds on
previous knowledge in an uncertain, path dependent and sometimes
discrete process, the introduction of a new technology cannot have unique
predetermined results”.
1.2. Social shaping of technology
In the early 1980s social shaping of technology emerged as a major school of
thought within STS. Under ‘social shaping10’ we find different streams, such as
social constructivism (SCOT) developed by Trevor Pinch and Wiebe Bijker,
actor-network theory (ANT)11 developed principally by Bruno Latour and Michel
Callon, and the systems model developed by Thomas Hughes12. Social shaping
models stress “that technology does not follow its own momentum nor a rational
goal-directed-problem-solving path but is instead shaped by social factors”
(Bijker, 2001: 26). These studies are the result of combining sociology of
scientific knowledge and history of technology (Pinch, 1996).
What are the main ideas behind social shaping of technology:

Technology is seen as a dimension of society rather than as an external
force acting on it from an ‘epistemological or metaphysical’ beyond.

Technology does not follow its own pace but is instead shaped by social
factors.

Technology is open to external forces, negotiation and change, while it is
designed.

Opening up the ‘black box’ of technology, to show how technological
artefacts are developed, and the different technical alternatives and paths
available.

How technological artefacts are changed by the users.

How technological artefacts are culturally constructed and interpreted,
although focused on the actors (i.e. agency-centered).
The study of the social construction of the bicycle by Trevor Pinch and Wiebe
Bijker (1989) has become a classic, as has the volume in which it appeared,
which Pinch and Bijker edited with Thomas Hughes (Bijker, Hughes, & Pinch,
8
1989). All subsequent work by both exponents and the critics of the social
construction of technology approach has taken this volume as its benchmark
(Fox, 1996). Nevertheless, the idea that technology is socially shaped is not
entirely new, Lewis Mumford talked about it in some of his books and articles.
Social constructivism is considered the current view or the core concept of
technology within STS studies13 (Cutcliffe, 2000: 52). Briefly its conceptual
framework consists of four related components:

Relevant social groups: are identifiable groups participating in the
construction/development of the technology, their ‘imprint’ their meanings
to the artefacts being developed.

Interpretive flexibility: neither an artefact identity, nor its technical success
or failure, are intrinsic properties of the artefact but subject to social
variables (Bijker, 2001). It is mainly during the design process that
different social groups give different meanings to the artefacts being
developed. However, final users can define what the technology is for and
may have a distinctive use from the one planned.

Closure and stabilization: the process of design and construction of a
technological artefact is not endless, it always comes to a closure and
consensus between the relevant social groups.

Technological frame (similar to scientific paradigm): structures the
interactions between the actors of a relevant social group (Bijker, 2001).
There are many authors highly critical of social constructivism, such as Langdon
Winner and Andrew Feenberg. Some of the most common criticisms to social
construction of technology, and generally to social shaping approaches, are:

These approaches are agency centered, leaving aside structural factors
and issues related to culture and power. As Klein and Kleinman put it:
“Following the actors, however, risks falling into a crude empiricism that
raises problems of its own” (Klein & Kleinman, 2002: 32).

They assume that all groups are equal and all relevant groups are present
in the design process.

They get lost in single historical case-studies. In Feenberg’s words:
“Constructivism’s narrow empiricism goes along with a purely academic
conception of the history of technology” (Feenberg, 1999: 11).

They study finished artifacts, when there is no room for changing or
negotiating any possible change. As Winner puts it: “True the new
methods are useful for historical study –reconstructing choices that have
9
already been made, speculating about how outcomes might have been
different” (Winner, 2001: 15).

They concentrate too much on how technology was developed
disregarding the impacts and effects of those technological choices.

Social constructivists assume technology as neutral and they do not take
political positions in this sense; they concentrate on how technology
works, on the form of that technology, and not on the social implications
(Williams & Edge, 1996).
Pinch (1996) responded to this last criticism: “What the social constructivists
work points to is that the design and adaption of technology should be part of the
political agenda. There is no one inevitable logic of development. There is
choice” (Pinch, 1996: 34). However, scholars within this tradition have not made
explicit recommendations on how to incorporate technology in the political
agenda (in contrast to constructive technology assessment).
Many authors have criticized social constructivist approaches, as well as
technological determinist positions. Both approaches could be seen as the two
ends of the spectrum, macro perspectives vs micro case-studies. What is in
between? There must be something, like a meso level methodology or approach
that recognize the co-evolution of technology and society. There are recent
developments in STS that could be named middle ground approaches, which
attempt to avoid either end of the spectrum; such as constructive technology
assessment and critical theory of technology. I will explore the former approach
in a following section.
Let me finish this section quoting Dorothy Nelkin (cited by Cutcliffe, 2000: 49),
when she asks what is STS and what are the purposes of the field:
“STS is still struggling with a framework and a mission. Is its purpose to
promote science, advance science, and frame policy that will advance
scientific and technological development? Or is it a form of criticism
focused on assessing and analyzing and critiquing science and
technology decisions? Is it theoretical analysis of science as an extension
of the sociology of knowledge and in effect a search for an understanding
of science and society dimensions? Or is a policy field intended to
engineer solutions to such dilemmas and to design new policies?”
Making reference to these questions, Cutcliffe affirms that various people would
have different answers and that those answers are all affirmative. In his own
words: “That is, STS has the potential, if viewed holistically, to be all those things,
which is not to say, however, that it can be all things to all people” (Cutcliffe,
2000: 49).
10
2. STP studies
Science and technology policy (STP) was established as an area of government
intervention in the immediate aftermath of WW II. Since then, there has been
some evolution in the focus of this area of public policy, both from a theoretical
and a practical point of view. Science policy in the Western world was
established in the 1940s. Initially, the main area of intervention and action was
just science. In the late 1960s, technology emerged more clearly as an area of
concern; due to budgetary constraints there was a need to be more efficient in
the allocation of resources and to ameliorate the impact of S&T on the overall
economy and society. In the 1980s, there was a shift to innovation policy. Some
analysts see the emergence of innovation as a particular field within science and
technology studies as a breaking point, a change of paradigm.
The evolution of Science, Technology and Innovation Policy (STIP) can be
explained mainly by political and economic factors, highly related to the world
economy. Biegelbauer and Borrás (2003) also mention that changes in STIP are
due to a notorious acceleration of innovation processes and the changing nature
of the state. Besides, social and environmental concerns have also played a role
in shifting emphases.
According to Metcalfe, technology involves much more than science, and
innovation involves much more than technology. Not all innovations involve the
application of technology (e.g. organizational innovation). Technology in itself is
of no significance unless it is translated into innovations. Innovation and diffusion
are primarily economic and social processes which involve many other actors
and behaviours besides those directly involved in the creation of technology
itself. In his own words: “technology policy, and science policy for that matter, are
best presented as aspects of a broader innovation policy” (Metcalfe, 2000: 405).
Science and technology policy - studies and practice - has been traditionally
divided between promotion and control. From a disciplinary perspective,
promotion has been influenced from economics and management theory.
However, control has evolved in a highly practical manner, with no influence from
the social sciences. One of the main criticisms to STIP studies, especially in the
field of promotion, is their lack of a critical stand-point towards scientific and
technological development.
STP studies have moved between two different models of science and
technology relationships (i.e. the linear model and the chain-link or interactive
model of innovation) and two major economic theories14 (i.e. neoclassical
economics and evolutionary economics of technical change). At present, within
STIP studies, and more precisely innovation studies, evolutionary theories are
the predominant paradigm. Both models and theories have quite opposite views
on how technological innovation occurs, and the policy instruments needed to
11
foster it. To explain in any depth these approaches is out of reach of this article,
so I will briefly outline the main differences between them.
The linear model of innovation implies that science (scientific research) leads to
technology (technological development) and new technologies satisfy market
needs. In other words, one does research, research then leads to development,
development to production, and production to marketing. Just one path, there are
no feedbacks during the process. From a policy perspective this implies a
concentration on funding (basic and applied) research, since this will
“automatically” lead to technological development, and later on to economic
growth. By contrast, the chain-link model, developed by Kline and Rosenberg
(1986), emphasizes the learning feedbacks between marketing, production and
development as a basis for the wider process of innovation. Interrelatedness of
different phases of the innovation process, and the feedback mechanisms are
involved. There are other authors that make reference to interactive or systems
models, similar to the chain-link model, anyway emphasizing that innovation do
not develop in a linear way, but on the contrary is a highly complex, uncertain,
accumulative and evolutionary process, which requires permanent learning. Even
if the chain-link model is recognized as the real way in which innovation happens
the linear model is still alive, especially in the policy-making communities; much
of the S&T policies proposed are supply-oriented (e.g. R&D funding).
Neoclassical economics sees technology as exogenous and being readily
available, and takes innovation for granted as an effortless and painless process.
“Neoclassical economics oversimplifies the issues of innovation by drawing on a
representative agent who attempts to reach an optimal equilibrium because he
acts in a deterministic or quasi-deterministic environment and is endowed with
perfect rationality” (Cantner & Pyka, 2001: 760). According to Lipsey and Carlaw
(2001), from a policy perspective, neoclassical theory gives preference for
policies that give generalized support to all R&D. In their own words,
“neoclassical theory presents an idealized form of all market systems. Its policy
advice is general, applying to all market economies operating at all times. The
advice is to remove market imperfections so that the economy can be moved
closer to the perfectly competitive optimum" (Lipsey & Carlaw, 2001: 13).
Richard Nelson and Sidney Winter are the pioneers of evolutionary theories 15.
They published in 1982 the book “An evolutionary theory of economic change”,
considered the landmark of this school of thought. In Nelson’s words16:
“Technical change clearly is an evolutionary process; the innovation generator
keeps on producing entities superior to those earlier in existence, and adjustment
forces work slowly”. Then Edquist adds: “The technologies that are developed
are only superior in a relative sense, not optimal in an absolute sense, and the
system never reaches a state of equilibrium. Technological change is an openended and path-dependent process where no optimal solution to a technical
problem can be identified” (Edquist, 1997: 6). Regarding policy advice,
evolutionary theories are designed to deal with all these characteristics of
12
technological change and because they lack of an optimal equilibrium we cannot
derive a set of welfare-maximizing policies (Lipsey & Carlaw, 2001).
In the late 1980s a new stream within innovation studies emerged, the systems
of innovation approach, being its precursors Christopher Freeman, Bengt-Ake
Lundvall, and Richard Nelson. The approach was developed from historicalempirical analyses, and it is based on evolutionary theories of technical change,
institutional economics, and the chain-link or interactive model of innovation. It
emerged thanks to findings in different research areas. On the one hand,
researchers and policy-makers saw how firms in different countries performed
differently, recognizing that national capabilities affect firms’ performance and
competitiveness. On the other hand, it was also recognized that firms do not
innovate alone; they rely on various supporting organizations and institutions.
[This approach will be further developed in a following section].
3. The divorce between STS and STP
Ina Spiegel-Rosing argued that STS studies and SPS (science policy studies)
have been traditionally divided, because of several reasons: their disciplinary
origin, their major sources of research questions, the consequent emphasis on
cognitive or operational problems, and their focus on science or technology
(Spiegel-Rosing, 1977: 17). 30 years later this view is still valid. She stated that
STS scholars have strong roots on their mother disciplines, various social
sciences (e.g. history, sociology, philosophy, anthropology, cultural studies),
implying that they have different intellectual traditions. STP, on the other hand,
evolved from political science and economics mainly, though is less fragmented.
Spiegel-Rosing made a clear difference in relation with scientometrics. In her
words:
“Scientometrics is not characterized by its focus on particular problem
areas but by its methodology, that is the use of quantitative indicators of
the structure and development of science in order to decide the basic
regularities of their functioning and direction. Scientometrics covers
traditional research areas of both STS and STP, and the fact that the data
produced is relevant to both fields. At least in this sense, in this sense, can
be said that scientometrics mediate between the two.” (Spiegel-Rosing,
1977: 18-19).
STS in its early years was mainly concerned with the study of science, neglecting
technology. Even today, technology is considered as a ‘minor’ subject of inquiry.
The split between science and technology has characterized academic research,
and in a lesser extent has been maintained in government circles. When STS
became an academic field, its research problems focused on understanding how
science functions and how knowledge is created. Meanwhile, STP became more
13
concerned with governance, direction and promotion in the real world of S&T.
Though, the former became concerned with the cognitive aspect of S&T, and the
latter with operational problems.
There is no general agreement on how to treat science and technology, whether
as a single concept or distinct spheres. The categorization depends highly on the
purposes. For instance, if we are trying to understand how knowledge is created
(the STS focus), is better to keep them as two distinct terms. Science and
technology are distinct branches of knowledge and distinct communities, located
in different institutional contexts, with different research problems and methods,
and respond to different incentives. From a policy point of view (the STP focus) it
makes sense to talk about ‘science and technology’ as a single concept, because
governments want to make the best of their investments on these activities, and
look for a single policy to build bridges and connections between science and
technology, to create synergies between the two. When the focus turned to
innovation, it became clear that innovation was much more than R&D, and was
more than technological development, innovation also includes organizational
and managerial factors affecting the S&T enterprise.
Peter Van Den Besselaar (2000, 2001) has undertaken several studies to
analyze the relationships between these scholarly communities. He divides the
field of science and technology studies in the same way we did at the beginning
of this paper: scientometrics or quantitative STS, qualitative STS, and policyoriented S&T studies. He found that the three sub-fields are differentiated from a
cognitive point of view, and the relations among them are as follows:




Qualitative STS is neither related to quantitative STS, nor to S&T policy
studies
None of the indicators used showed any interaction between qualitative
STS and policy-oriented STS.
Little relation between qualitative STS and quantitative STS. However,
scientometrics does refer to qualitative STS on a regular basis, the other
way round is almost absent.
The interaction between quantitative and policy-oriented STS is more
intimate and growing, especially related to the indicator studied, and to
R&D performance evaluations (Van Den Besselaar, 2000: 184; 2001:
442).
Van Den Besselaar He also found that there is not only cognitive differentiation
(e.g. topics studied, methods, co-citation) but also social and institutional
differentiation. He analyzed the most important journals in each sub-field17,
looking for authors and institutes that cross borders, that is, that publish in more
that one sub-field on a regular basis. He encountered that there are very few
authors and organizations that have expertise in more than one area, and
generally this second study confirmed the findings of the first one: there is hardly
a (social and cognitive) relationship between qualitative and policy-oriented
14
studies. He concludes that “no one STS field exists, but at most several loosely
related sub-fields studying the development of S&T” (Van Den Besselaar, 2000:
175). He adds that “if we take these findings together, STS seems to split apart
into two relatively stable and coherent fields, the qualitative and the policyoriented studies, while the position of scientometrics is in between, but more
strongly linked with the policy-oriented studies" (Van Den Besselaar, 2000: 184).
Another difference between STS and STP lies on the audiences. For STP it is
clear: their audience is policy makers and business people. But for STS is not
that clear. Is it the academe or the general public? It seems that they are not
clearly defined for STS, or that the potential audiences have been unreceptive,
taking into consideration that people, in general, still tend to think about science
as some neutral, value-free, objective activity, and about technology in a
deterministic way, characteristics that have been refuted in many academic
works.
4. How to build a bridge between STS and STP
By integrating different bodies of literature, combining sociological-philosophical
approaches with economic-management perspectives, one can start to build a
bridge between STP and STS. We need to bring into the STS debate a set of
concepts from political economy and organizational analysis, and suggest how
pulling these concepts together can provide a cogent approach to analyzing
technology development (Klein & Kleinman, 2002). My first attempt is to combine
the systems of innovation approach and constructive technology assessment,
from a theoretical point of view, looking at points in common and
complementarities between the two. Different lenses could provide us with a
comprehensive view of technical change and innovation and how to ‘direct’ their
impacts upon society.
As noted above both STS and STP studies have methodological problems, both
areas of studies have various stream that focus either on micro case studies or
macro analysis, neglecting the meso level. By combining the systems of
innovation approach and the principles of constructive technology assessment
maybe a “bridge” could be achieved. Starting in STS, constructive technology
assessment (CTA) can be situated between micro and meso levels of analysis.
On the other hand, within STP there have some recent developments that can be
situated at the meso level, such as studies about regional systems of innovation,
sectoral innovation systems, technology systems, and innovation networks.
4.1. Constructive Technology Assessment (CTA)
Technology assessment (TA) as traditionally understood has evolved overtime.
Initially TA was done at the end of the ‘tube’, when the technology was already in
15
use and deployed. "From an STS perspective the emphasis shifted several years
ago from ‘impact assessment’ to design, social shaping, and the management of
technology in society” (Rohracher, 2004). Today, technology assessment can be
categorized in many ways: awareness TA, strategic TA, and constructive TA.
Constructive technology assessment (CTA)18 is also referred by different authors
as interactive TA or participatory TA. According to Schot & Rip the idea behind
technology assessment is to reduce the costs of societal learning in the use of
new technologies, by anticipating potential impacts and feeding these insights
into decision making (Schot & Rip, 1996).
Gow (2003) explains the main differences between TA and CTA, based in two
distinct models of technology development; in this own words:

Exogenous model: anticipates a finished technology that enters into and
creates effects in a society.

Endogenous model: recognize that a technology and its effects are not
necessary dropped ‘stork-like’ into a society but are produced ‘womb-like’,
by various interested parties within society.
He further says that “CTA redefines technology assessment as an active
contribution to the process of design as opposed to an independent program of
technology impact analysis” (Gow, 2003: 42). The shift in focus within TA
responds to the need to recognize that empirical studies of actual impacts are not
enough, that the analysis of possible impacts in plausible scenarios is necessary.
CTA attempts to develop technologies with desired positive impacts and with few
(or at least manageable) negative impacts. Therefore, the moment of intervention
is crucial, and should be during the design phase. That does not mean that it is a
simple and straightforward task. “First of all, which impacts we want to consider
and in whose interest? Second, impacts are dynamic. Therefore, even if clear
values are present and shared, it is often impossible to identify an optimum
strategy beforehand. This implies that experimentation and societal learning must
be an integral part of management of technology in society” (Rip, Misa, & Schot,
1995: 4).
CTA has its own methodological problems, in particular what is known as the
‘Collingridge dilemma’, the knowledge vs control dilemma. When control of a new
technology is still possible, that is in the early stages of design and development,
knowledge of possible impacts is limited because these are difficult to anticipate.
Yet once the technology is deployed it may be insuperably difficult to introduce
substantial change to it, because of investments made in its development and
deployment, so it can hardly be controlled (Gow, 2003; Rip, 2001; Rip et al.,
1995).
16
Some authors believe that this is a major obstacle for CTA, in that is very difficult
to anticipate impacts, and if intervention occurs to be right. However, Rip affirms
that:
“Anticipations need not to be correct to be useful in guiding action
productively. We need visions of the future in order to orient our actions
and this is what recent TA activities attempt to provide. One could call this
the agenda-building function of TA, in contrast to the forecasting and
assessment function which is also there, and was its main function in the
1960s" (Rip, 2001: 197).
Going back to the issues that have contributed to build a gap between STS and
STP, wit was mentioned the little interest and impact that STS practitioners have
had on policy-making. Based on the principles of CTA, it is clear that one of the
objectives of this stream is to affect technology and policy design.
4.2. The Systems of Innovation Approach
Innovation studies are dominated by various paradigms, such as evolutionary
theories of technical change, systems of innovation approach, and theories about
interactive learning19. The systems of innovation (SI) approach provides a
conceptual framework to understand the complexities of the innovation process
and the institutional arrangements that affect it. It can be argued that the SI
approach has been the policy response to evolutionary and institutional theories;
it is the translation of economic theories into a frame of reference for policy
purposes. The SI diffused quickly and became very popular among researchers
and policy makers, although it is neither a theory nor a policy prescription.
Quoting Castellaci and colleagues, “the SI concept may serve as a focusing
device for development of industrial and innovation policy instruments”
(Castellaci, Grodal, Mendonca, & Wibe, 2004: 9).
Knowing that firms do not innovate alone, and that the environment affects their
innovative capabilities, the SI approach helps to decipher that environment.
Institutions, networks, knowledge creation and diffusion, and learning are the key
issues. Within the systems of innovation approach we find different levels of
analysis, national, regional, sectoral, and technological. Here onwards, I would
make reference to national and regional systems of innovation, taking into
account that most of the literature and empirical studies available deal with these
two. For some reason -which I do not know- a handful of scholars are dedicated
to study sectoral innovation systems20 and technology systems21.
With the exception of the national system of innovation approach, where the unit
of analysis and the boundaries of the system (the nation-state) are clearly
established, it is difficult to circumscribe other levels of analysis. For instance,
Doloreux and Parto (2005) discussing how to define a region, affirm that the
17
debate on the appropriate scale of regional systems of innovation is far from
resolved: is it a region, a province/state, an agglomeration of cities, a city, a
metropolitan area, or a locale (part of a city)? A technology system could make
reference to an specific technology in the sense of knowledge field, to a product
or an artifact, or finally to a set of related products or artifacts aimed at satisfying
a particular function (e.g. transport, communication, health care) (Carlsson,
Jacobsson, Holmen, & Rickne, 2002).
There is no accepted or unique definition of national systems of innovation (NSI),
and few differences can be seen when compared with definitions of regional
systems of innovation, apart from the latter making reference to a limited
geographic area within a nation-state. For the purposes of this paper Metcalfe’s
definition is clear and detailed:
“A system of innovation is that set of distinct institutions which jointly and
individually contributes to the development and diffusion of new technologies
and which provides the framework within which government form and
implement policies to influence the innovation process. As such is a system of
interconnected institutions to create, store and transfer the knowledge, skills
and artefacts which define new technologies” (Metcalfe, 1995: 462).
Regional or local systems of innovation move the focus to spatial aspects which:
“Has two major advantages; on the one hand, it recognizes that innovation
is a social process and is shaped by persons and institutions that share a
common language, rules, norms and culture (i.e. common modes of
communication). On the other hand, innovation is also a geographic
process, taking into account that technological capabilities are grounded
on regional communities that share a common knowledge base” (Holbrook
& Salazar, 2004: 51).
In moving from national to regional innovation systems the organizational and
institutional framework becomes, paradoxically, less clear at least in terms of
government, despite the smaller and apparently more manageable nature of the
system. This is because “regions are neither autonomous nor sovereign in terms
of relations with the nation-state or supranational institutions” (Braczyk &
Heidenreich, 1998). To define the boundaries of the system is crucial but not an
easy task, especially when the unit of analysis is the region. Two main criteria
could be used to identify a ‘region’ for innovation purposes: geographic and
administrative. The most common uses administrative boundaries, useful
especially from the point of view of availability of data and policy-making. In
addition, Edquist (2005) proposes that the geographic area chosen have a high
degree of ‘coherence’ or ‘inward orientation’ regard to innovation processes.
18
Let’s summarize the characteristics of SI approaches that Edquist identified in his
book in 1997, which was later re-phrased by Edquist and Hommen (1999), as
follows:









They place innovation and learning processes at the center of the focus.
They adopt a holistic and interdisciplinary perspective.
They employ historical perspectives.
They stress the differences between systems, rather than the optimality of
systems.
They emphasize interdependence and non-linearity.
They encompass product technologies and organizational
innovations.
They emphasize the central role of institutions.
They are still associated with conceptual diffuseness.
They are conceptual frameworks rather than formal theories.
From a policy perspective, arguably the most of important impact that the SI
approach has have upon innovation policy is to point at the design of policy in a
consistent and coherent manner, i.e. single policies have to aim to a common
goal, to improve the nation’s innovation performance. The idea is not to propose
stand-alone policies, but to design a portfolio of policy instruments, in order not to
just to enhance individual elements of the NSI but the system as a whole.
In this sense, the SI approach makes emphasis on the analysis of systemic
failures not market failures, and shifts state intervention from just funding (supplypolicy) to attempts that the innovation system performs adequately as a whole.
As Guy and Nauwelaers note: “From a conceptual perspective, embedding STI
policies within the context of a systems framework provides a strong argument
for the development of ‘systemic’ policies in addition to ‘reinforcement’ and
‘bridging’ policies. It also necessitates an appreciation of weak spots in current
policy mixes and the formulation of appropriate steps to rectify these
weaknesses” (Guy & Nauwelaers, 2003).
5. Concluding remarks: Common areas of research
Although, both STS and STP have developed separately, building, to date, little
or nothing on one another, these two areas converge around very important
issues: both recognize that technologies are embedded in social relationships
and that they produce a series of unintended consequences, and the process of
technological development and innovation is full of uncertainties There is a
common area of interest, the necessity to develop the meso level of analysis,
which has potential in both STS and STP studies. The connections between the
meso level and the macro level of analysis have to be worked out. It seems
easier to go downward (meso and micro linkages) than upward (meso and macro
connections).
19
Scholars within the technology assessment tradition propose that we need
visions of the future to orient our actions, what they refer to as the agendabuilding function of TA. Similarly, innovation scholars and policy analysts have
been doing (technology) foresight exercises for some time now. From the
outside, without having reviewed specific studies by TA practitioners, it seems to
me that they are doing the same kind of analyses and are pointing in the same
direction, anticipate impacts and direct technology. It would be very interesting to
explore how each field can contribute to understand the dynamics of technology,
how to reduce uncertainties about impacts, how to orient its future developments,
and how to create a ‘history of the future’ (using Rip’s terminology). Clearly, a
future area of research is to analyze and compare empirical studies in CTA and
technology foresight.
The systems of innovation approach is a good tool for determining the main
actors involved in the innovation process, as well as the - local or national capabilities needed to foster innovation. However, scholars within this approach
have not been very successful at explaining communication and interaction
patterns, and how networks function. Certain STS approaches could possible
contribute to the unfolding of these issues. On the other hand, empirical studies
about systems of innovation have had little influence on CTA studies, helping to
identify the main stakeholders that could participate in technology assessment
exercises, doing institutional and organizational mapping and tracking knowledge
flows. Future work should explore in more depth technology systems22, sectoral
innovation systems23, and socio-technical systems24 in order to see how they can
relate to CTA.
STS approaches have focused mainly on individual technologies or technological
systems but not on innovation systems and networks. However, technology
usually develops in larger structures and/or networks, with the participation of an
array of actors such as firms, public organizations, and social and legal
institutions; though the importance of system approaches. Innovation occurs in
institutional, political and social contexts, it is embedded in social relationships.
Consequently, STS and STP studies, if combined, could provide a more cogent
view of technological development.
This is a revised version of a paper presented at “Science and Technology in Context: An Interdisciplinary
Graduate Student Conference”, Washington D.C., April 23-24 2005
2 E-mail address: msalazar@sfu.ca, Tel: 1-604-291 5257, Fax: 1-604- 291 5239
3 See for instance Jurgen Habermas, Toward a Rational Society, 1970.
4 See for instance Jacques Ellul, The Technological Society, 1964.
5 Evolutionary theories of technical change started to emerge in the late 1970s, early 1980s.
6 A techno-economic paradigm, concept developed by Perez and Freeman, refers to an economy’s set of
interrelated technologies, with the supporting and facilitating policy and infrastructure.
7 A technology regime is a coherent set of techniques that characterize a specific historical period of time.
1
20
8
See Freeman and Louca (2001) for a discussion of modern industrial capitalist economies -how they
change, how they structure their change, and how these patterns of change configure long-term fluctuation,
known to economics as long waves or Kondratiev waves”.
9
I would like to thanks Dr. Richard Lipsey, who made available the first chapter of the forthcoming book
“Economic Transformations: General Purpose Technologies and Long Term Economic Growth”, wrote with
Kenneth Carlaw and Clifford Bekar.
10
Sometimes social constructivism is used as the concept ‘umbrella’ that covers these different models, but
I considered social shaping a more encompassing concept.
11 Cutcliffe has a short and clear definition of this approach: “The key concept is “actor network” – a group of
entities that includes not only people but also theories, technical devices, political institutions and policies,
even the natural environment. Together this network of animate ‘actors’ and inanimate ‘actants’ constitutes a
seamless web. These heterogeneous elements are all equally important and must be considered
‘symmetrically’, that is, as equally important” (Cutcliffe, 2000: 31).
12 “The systems approach analyzes technology as heterogeneous systems that in the course of their
development acquire a technological momentum that seems to drive them in a specific direction with certain
autonomy. Hughes explicitly makes the argument against a priori distinction between the social, the
technical, the scientific, and so on. The concept of technological momentum nicely captures the seemingly
autonomous nature of technological systems, while at the same time showing that it is not an intrinsic
property but is slowly built up during the systems development” (Bijker, 1995: 250). Thomas Hughes is
usually situated within social constructivism, although he positioned himself in between technological
determinism and social constructivism.
13 Other authors considered ANT as the most successful theoretical achievement within STS so far
(Sismondo, 2004).
14 There are also some organizational and managerial theories that have informed STIP, especially looking
at how innovation occurs within the firm.
15 It is important to mention that evolutionary theory has been focused, since it emerged, on explaining how
technology is created and how it evolves over time, compared to other economic theories that their main
focus have been explaining growth, development, trade patterns, or market behaviour.
16 Nelson, R. (1987). Understanding Technological Change as an Evolutionary Process. Amsterdam:
Elsevier. Cited by Edquist, 1997:6.
17
The journals he used were: Social Studies of Science and Science, Technology and Human Values for
the qualitative area; Research Policy for the policy-oriented sub-field; and Scientometrics for quantitative
studies.
18 It is important to note that CTA originated in the Netherlands; following Denmark, Norway, and Germany.
To my knowledge not many more countries have applied this approach.
19 Purposely, I am leaving aside management theories related to innovation, which are not relevant for our
discussion.
20 “A sectoral system of innovation is a set of products and the set of agents, carrying out market and nonmarket interactions for the creation, production and sale of those products” (Malerba, 2002: 247).
21 A technological system is a network of agents interacting in a specific economic/industrial (technological)
area under a particular institutional infrastructure or set of infrastructures and involved in the generation,
diffusion and utilization of technology. (Carlsson & Stankiewitz, 1995 cited by Edquist, 1997: 8). It is focused
on generic technologies with general applications over many industries.
22 See for instance Carlsson & Jacobsson (1997) and Kash & Rycroft (1998).
23 See for example Breschi & Malerba (1997) and Malerba (2005).
24 See for instance Geels (2004).
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