Saurabh Arora

266 Pages·2008·7.69 MB·English

by  Saurabh Arora

Ph.D. Thesis
Coherence in socio-technical systems: a network
perspective on the innovation process
Saurabh Arora
UNU-MERIT
Table of Contents
1. Introduction 1
1.1 Outline of the thesis 11

Part I: Technological interdependence and evolution
2. Technological interdependence and knowledge flows 15
2.1 Mechanisms of technological interdependence 16
2.1.1 Technology succession and interdependence 20
2.1.2 Horizontal interdependence in complex products and technology systems 21
2.1.3 Generic technologies 24
2.1.4 Vertical relationships 27
2.2 Temporal aspects of interdependence: gradualism and discontinuities 30
2.2.1 Gradualism and persistent technological interdependence 31
2.2.2 Radical innovations or technological discontinuities 34
2.3 Structure of technological interdependence 39
2.3.1 Hierarchy in systems: core and periphery 40
2.3.2 Circular flow of goods and knowledge: a structural 'tableau' for economic
48
and technological interdependence

3. The growth of knowledge and complexity in an evolving network model
69
of technological innovation
3.1 Models of technological evolution 71
3.2 The model 76
3.2.1 Model dynamics 77
3.3 Simulation results 79
3.4 Simulation experiments: different core strengths 89
3.4.1 Core-strength 90
3.4.2 Number of knowledge types mutated in one time-period 95
3.5 Discussion 97

Part II: Social interactions and social capital
4. Introduction: networks of a rural community 100
4.1 The community in focus 100
4.2 The story of pest control in the village 104

5. Network data and approach used for data collection 109
5.1 Approach for collecting data on knowledge flows in the village 111
5.2 Business network data 115
5.3 Collection of social network data 117
5.4 Measuring innovation capacity: knowledge diffusion and experimentation 120
6. Innovation capacity of the farmers and the knowledge network 122
6.1 Agricultural innovation: a brief overview 122
6.2 Farmers' innovation capacity in NPM 124
6.3 The knowledge network 130
6.4 Discussion 136

7. The farmers' escape from pesticides and debt 139
7.1 The business network: accessing agricultural inputs 139
7.2 The dominant core in the business network 142
7.3 Discussion 143

8. Socio-political events and social network structure 145
8.1 Village India and social networks 146
8.2 Collective action and media success 153
8.3 Power, influence, centrality, and brokerage 154
8.3.1 Power and influence in a social network 155
8.3.2 Brokerage and social capital 160
8.4 Community leaders in the social network and group action 167
8.5 The rift in the face of unity 171
8.6 Conclusions 173

9. Embeddedness of knowledge flows in social network 176
9.1 Embeddedness of economic and technological activities, and social capital 177
9.2 Measures of structural embeddedness 185
9.3 Embeddedness of knowledge flows in the local innovation system 190
9.4 Social embeddedness of knowledge flows in the community of
204
agriculturalists
9.5 Discussion 208
9.6 Conclusions 210

10. Summary and conclusions 214
10.1 Summary of the results 214
10.2 Implications for rural development interventions 221

References 225
Appendices 243

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1. Introduction
Continued economic growth depends on technological innovation and the underlying
accumulation of knowledge. However, the successful development of new technologies
and their post-adoption impact on productivity growth require parallel changes in
complementary technologies and socioeconomic organization. This implies that
technological change must be seen in the context of complex socio-technical systems.
Coherence in such multi-technology, multi-actor systems implies two things: First, that
changes in one technology do not impede but rather aid the innovation process in other
technologies; changes in the interdependent technologies positively reinforce each other.
Second, that decentralised action by relevant networks of actors promotes the
development and use of desired technologies. The latter aspect of coherence requires an
effective combination of different forms of social capital possessed by individual actors to
make the innovation project sustainable.
Individual technologies are always embedded in larger multi-technology systems.
Technological interdependence within and between these systems plays a central role in
directing the dynamics of the innovation process (Rosenberg 1982; Tushman and
Rosenkopf 1992). This is particularly apparent today when technological systems have
become highly complex: made up of intricate networks of coupled technologies. As the
philosopher Langdon Winner (2002) has warned, in such tightly interdependent systems, a
small failure in one of the components can cause the collapse of the entire system.
Consider the recent Internet ‘blackout’, which affected more than 75 million people in
South Asia and the middle-east, only because two submarine cables were snapped by an
unwieldy ship in the Mediterranean (The Guardian, February 1 2008). Ripples of the
blackout were felt throughout the world as firms in the US and Europe failed to reach
their business contacts such as outsourced call centres and software developers in India,
Egypt, Pakistan, and Dubai. Commenting on the blackout, ICT experts echoed Winner in
pointing to the fragility of our densely interconnected technological systems.
This example shows that the inherent complexity in technological systems may cause
fragility and threats to sustainability of complete and stabilised systems made up of fully-
developed technologies. In addition, such complexity must be acknowledged in the
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process of development of new technological systems. There are two ways in which this
latter complexity emerges: first, individual technological innovations are generally novel
combinations of existing inventions (Usher 1954). Second, the newly developed
components of a system must fit with each other for a system to fulfill its functions.
Similarly, the successful introduction of a new technology into an existing system requires
that modifications be made, both in the new technology and the existing system-
components to which it gets connected. Failure to acknowledge the importance of these
systemic interconnections has, in the past, resulted in socially costly technical change
(nuclear power, or numerical control automation in the US: Noble 1984; Cowan 1990).
In order to grasp the complexity of systemic technical change, economists have
identified and analysed a set of key technologies known as general-purpose technologies
(David 1990; Bresnahan and Tratjenberg 1995; Helpman 1998; Freeman 2001). Sustained
economic growth is strongly correlated with the economy-wide diffusion of general-
purpose technologies (GPTs) such as the steam engine or the computer. However, such
widespread diffusion often occurs slowly, over the course of several decades largely
because the successful introduction of any major new technology involves significant
changes in the technologies comprising an existing system and the organization of
factories where they are used. For example, to utilize the unit drive electric motor in the
first few decades of the 20th century, extensive modifications were required in existing
factories where manufacturing technologies were run on the group drive system of power
transmission suited to water- or steam-power (David and Wright 1999).1 The focus of the
GPT studies by and large has been on this gradual (post-innovation) diffusion of the
GPTs, accompanied learning, and the resulting impact on productivity growth. Little
emphasis has been placed on the interdependencies in the pre-diffusion, developmental

1 The first electric motors in these factories were often used to run a group of related machines, just like
with water- or steam-power, instead of the more efficient unit drive system of powering each machine
with its own electric motor (David and Wright 1999: 23-25). Only through decentralised learning by
electrical engineers and other skilled personnel in different locations over decades, were the factories
gradually able to switch to the unit drive system. Eventual benefits of using the latter system included
zero investments for a prime-mover in a factory, cheaper factory construction as lower structural
strength is required to use individually powered machines rather than the heavy transmission equipment
associated with water- or steam-power, more flexibility in the operation of the factories such as better
speed control made possible on individual machines, and benefits due to the modularity of the new
system as only a small section of factory now needed to be shut down for maintenance/repair (David
and Wright 1999: 25).
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phase wherein the knowledge that underpins these technologies is created, and the links
that make them general-purpose are spread through the socio-technical system.
The pre-diffusion construction phase of technologies has been extensively studied by
sociologists and historians of technical change who were interested in changes and
realignments that are common and indeed possible before a technology has stabilised. To
deal with the complexity of technology development, these studies have developed
different approaches including the large technological systems approach developed by
Hughes (1987), the actor-network synthesis of Callon (1987), and the concept of
sociotechnical ensembles due to Bijker (1995).2 These and related studies (for example,
Latour 1996; and Mackenzie 1996) demonstrate that technological research and
development does not follow a ‘natural trajectory’ or internal technical logic, or possess an
autonomous technological momentum. According to this view, technological innovations
are socially constructed and sustained, and social relations are shaped by technological and
economic factors. These social constructivist studies have provided valuable empirical
insights into the wider systemic interactions between different social groups (such as
workers, engineers, scientists, users, regulators, and industry organizations) and the
technologies they collectively shape and are in turn shaped by. They force us to consider
that development of individual technologies is embedded in a set of social networks of
actors. These networks not only allow effective sharing of knowledge but may also act as
conduits for socio-political support, trust, and conviviality (and serve as the field on which
competitions between different technologies, or visions of a technology, are played out).
In this dissertation, I develop an alternate way to interpret and grapple with the
complexity of socio-technical systems, both while they are developing and when stabilised
systems come under threat from newer competing technologies. In particular, I view a
socio-technical system, and its network of underlying knowledge types, as having a core-
periphery structure. The structure is not pre-defined, but rather emerges from
technological and social interactions. Once can imagine a generic sort of history wherein a
new system is ‘formless’ at an early stage of development when different new technologies
(or versions of a technology) are tried out. During this nascent phase, there is no clear
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structure in which different parts of the system are organized. The emergence of a core
brings the system out of its nascent experimental phase, launching a coherent phase of
knowledge accumulation around a small number of core elements. Elements in the core
are connected to each other in a positive feedback loop: changes in one core element send
reinforcing signals to other core elements. This positive reinforcement makes the core self-
sustaining and gives it the dynamism to support innovation in peripheral (complementary)
elements of the system, which are directly or indirectly connected to one of the core
elements. Thus, as a result of this coherence, changes in one technology do not impede,
but aid innovation in other interdependent technologies and make the entire system
‘dynamically sustainable’.
The coherent core in a technological system is similar to the ‘hard core’ of a scientific
research programme, that is a sequence of theories and a heuristic principle guiding the
direction of research, highlighted by Lakatos (1978). The hard core of a programme is
immune to falsification and surrounded by a “protective belt” of ‘auxiliary hypotheses’
which “bear the brunt of tests and adjusted and re-adjusted, or even completely replaced,
to defend the thus-hardended core.” (Lakatos 1978: 48). Similarly, in an evolving
technological system, selection pressures first hit the peripheral components which get
modified or replaced while the core remains intact. However, in a stabilised or mature
system, the possibilities for further knowledge accumulation are limited and sustaining the
innovative dynamism of the core and periphery becomes difficult. Lakatos observes
similar stagnation in degenerating research programs which fail to produce any new
predictions and expend a lot of effort in explaining away the anomalies. Such a research
programme in science (and technology) comes under threat from a new competing
programme, which replaces an existing mature programme only if it is stronger (or more
progressive) than the latter. Here strength is determined by the novel theoretical
predictions produced by the research programme (“theoretically progressive”), and
corroboration of the predictions using empirical data (“empirically progressive”). These
theoretical and empirical results are produced according to the research programme's
heuristic principle rather than by dealing with an anomaly through ad hoc adjustments to

2 In addition, Bijker (1995) uses concepts such as interpretive flexibility, closure, technological frames,
and relevant social groups.
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the programme (Lakatos 1978: 179). The eventual replacement by the newer research
programme or technological system is generally referred to as a revolution or paradigm
shift.3 Although technological systems are distinct from scientific research programmes in
a number of ways, the hard core-auxilliary hypotheses framework of Lakatos corresponds
well with the theoretical framework developed in this thesis for understanding the
evolution of a technological system. Akin to Lakatos’ arguments about competition
between rival research programmes, the survival of an existing technological system when
faced with competition from a rival system depends on the strength of its core and the
protection and support it receives from the periphery.
Returning to the arguments of the social constructivists: the development of a
technology, apart from being shaped through interactions with other interdependent
technologies, is driven by a number of socioeconomic actors including firms (or farmers in
the case of agriculture), engineers, scientists, policy-makers, regulators, and end-users.
Together these actors (with embodied knowledge and skills) and their interactions; the
societal institutions that govern their behaviour; and the technologies they develop and use
may be said to constitute a system of innovation.
In the knowledge sphere of an innovation system, the multiple knowledge types
underlying most modern technological innovations, often originate in different branches
of science and engineering, such as micro-electronics, software programming, and
mechanical systems in the case of CNC machine tools. These knowledge types routinely
interact with each other in the construction of new technologies.4 The necessary expertise
in the different interdependent (complementary and otherwise interrelated) knowledge
types is distributed across multiple actors, necessitating interaction among the actors. In
addition, the users of a technology are often important contributors in the innovation
process, sometimes as the most important sources of new innovations (von Hippel 1988).
Then, collective learning through the exchange of knowledge in systems of innovation (or
communities of practitioners) forms the basis of innovative activity. Innovative

3 Note the reference to Kuhn’s (1970) analysis of normal science and revolutions through shifts in
scientific paradigms and to Dosi’s (1982) adaptation of Kuhn’s ideas for studying technological
development by using the concepts of technological trajectories and paradigms.
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performance of the actors and eventual shape of a technology are functions of the
structure of the knowledge (-sharing) network of an innovation system (or a community of
practitioners). There are three primary reasons why knowledge networks are critical for
learning, innovation and innovative performance,
a) Innovation generally is a result of a new combination of diverse knowledge types
which must positively feed into each other in order to provide a coherent whole.5
This is as true for complex innovations in bio-pharmaceuticals as it is for Watts’
steam engine. The latter for example required critical input from the knowledge of
machine tools to drill cylinder bores of desired accuracy (Rosenberg 1976).
b) Diverse knowledge types which constitute technological systems ordain that no
single actor can possess expertise in all knowledge types. Interaction between
actors possessing expertise in interdependent technologies is necessary not only
for knowledge sharing but also for the regular functioning of a system.
c) Close interaction between actors is also critical for the transfer of tacit knowledge
between a) the ‘experts’; b) experts and the users; and c) among the users (von
Hippel 1988). Tacit knowledge is believed to be harder and slower to acquire than
its codified counterpart. Transfer of tacit knowledge generally requires extensive
face-to-face interactions facilitated by close social relations (Nelson and Winter
1982; Cowan et al. 2000).
Thus the sharing or exchange underpinning collective learning is not restricted to the
knowledge sphere alone. Other forms of economic and non-economic exchanges in
networks of social relations are believed to provide the substratum on which the
interactive learning takes place, due to the tacitness of some knowledge (see the third point
above). This importance of social capital for technological learning, and resulting

4 The knowledge types relate to the use, design, and production of technological artefacts on the one
hand, and more basic knowledge of principles and facts on the other.
5 This statement is widely believed to be applicable only to radical innovations, as opposed to
incremental innovations. However, as I argue in more detail in chapter 3, despite its unitary appearance
a radical innovation may in fact be a combination of many incremental innovations. It may be treated as
a gradual evolutionary process involving many incremental steps (Usher 1954; Silverberg and
Verspagen 2004).
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innovation, is underlined in the prolific literature on systems of innovation and
communities of practice (see for example, Edquist and Johnson 1997; Lundvall et al. 2002
on the former; and Lave and Wenger 1991; Brown and Duguid 1991 on the latter).6
However, the literature generally assumes that social networks simply double as
knowledge networks i.e., the same links that transmit trust and conviviality are assumed to
transfer knowledge as well. In this thesis, I investigate the extent to which this assumption
of equating social ties to knowledge flows is justified. And if the assumption turns out to
be mistaken, do we have to re-think the nature and importance of social capital for
learning and innovation in socio-technical systems?
Even in the wider literature on the economics and sociology of networks, the content
of links is rarely the analytical focus. As a result, after reading many a study on the virtues
of social networks and social capital, one is left wondering what the authors actually meant
by the ‘social’ beyond the impression that it is something beneficial.7 In this thesis, I attach
specific meanings to the social in three ways: first, close kinship and friendship ties that
transmit socio-political support, trust and conviviality; second, ties that transmit problem-
solving knowledge; and third, ties that are used for exchanging goods (farm-inputs, credit,
and crops produced by farmers). The three different networks focus on a single agrarian
community in Andhra Pradesh, India, where a technological transition from pesticides to
non-pesticidal management (NPM) of crops was attempted in the last decade. I analyse the
structures of the three networks using the core-periphery analogy and the theoretical
results on competition between rival systems based on core-strength from part I of the
thesis.
Based on the foregoing discussion, one may naturally ask why is it critical to
distinguish between different types of social capital? To answer this question, we must first

6 This social capital is generally translated as trust and common vision internally in the community or
innovation system and a common united face showed to the oustide world. In addition, of course it is
believed to facilitate the transfer of tacit knowledge, as noted earlier.
7 There are other problems with the concept of social capital. For example, since the mid 1990s social
capital has become a cause and an effect of anything virtuous happening to an individual, group or even
a society as a whole: so it is the cause and effect of beneficence at multiple levels. This and other
confusions, in interpreting and understanding social capital, precipitated after the work of Robert
Putnam and colleagues (1993). More details in Chapter 8 of the thesis.
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