Lecture 1
Introduction to the course; History and overview of Innovation Studies
“Transcending received wisdom is the essence of innovation”
~ Anderson and Tushman, 2004
→ going beyond the obvious
→ central point of following a master programme in general, you also want to
learn new things, go beyond the current theories such as improving theory while
writing your thesis
What can innovation theories do?
- explain → if A is like this, B is like… because of …
- predict → if A is hypothetical state, you can predict hypothetical state of B
- infer
- simplify → making a model out of a complex situation, putting it into a
simplified representation of reality
- summarize → it is also about describing, describe what is happening
through a theory → theory: taxonomy, classifying how the world would
look like
- compare → what you can do with the theory (comparing countries with
each other for example)
As an innovator, you want to predict whether your product will be success and how fast
it will be adopted (diffusion) as it impacts your revenue streams
Adoption is more micro-level → individual, part of organization → decision to
adopt an innovation
Diffusion is aggregation of adoption individuals → number of adoptions among
the week for example → S-curve
How to explain implementation success → buy product, but how to implement it
into your organization?
On the slide:
- addressing problems of new products, new knowledge, new practices →
economic and societal embedding of new technologies
- adoption, use, acceptance (whether we as a society want such a
technology → ethical debate → GREAT example of Sander: cloning),
diffusion, rejection of new technologies
- grasping these problems: 1) identify patterns (describe), 2) provide explanations
(explain), 3) give advice (predict)
- formulate improved understanding & give advice how to do better
General assumptions, classifications, laws, propositions, correlations,
assumptions, observations etc. are words that are back of your mind when
,thinking of theory → article of Sovacool, B. K., & Hess, D. J. (2017). Ordering
theories: Typologies and conceptual frameworks for sociotechnical change.
Social studies of science, 47(5), 703-750. (first 6 pages only) → provide
perspective through which you can look at the world and make sense of the
world, making it empirical as well → testing it through hypothesis with
quantitative or qualitative theory
“theories can service as important heuristic devices that enable researchers to make
sense of large amounts of data”
“there is no agreed upon definition of theory”
Assignment (individual) → choose 1 article
- core quote
- short (300 words) excerpt of the article with its main argumentation
- visualization: create an overview of the most important mechanisms involved in
the theory
- reflection: what does it mean → under which circumstances can the theory
be applied (200 words)
- formulate 2 questions that the article raises to you
Strength of theory: gives the opportunity to say something of the future → data
is from the past, through a lens of theory you can determine how a certain
mechanism has worked in the past (based on data) and conclude if it could be
applicable to an issue in the future
Not saying “what to think” but “how to think”
History of the innovation studies
- linear model → not so linear as we are seeing. But still used because it is
straightforward, regulation dictates it (from one stage to another stage →
clinical trials for example: you can only go further if you complete
something)
- basic research → frontier of science without a clear purpose (the
role of DNA slicing for example)
- applied research → developing new scientific knowledge with a
commercial objective (how to make use of DNA slicing for example)
- development → prototype (produce first humanized insulin →
biotechnology)
- some of political entities favor applied research over basic research
because it has economic value, it is beneficial for companies (funding top
sectors to stimulate economic activity)
- “Basic research creates the fund from which the practical
applications of knowledge must be drawn” ~ Vannevar Bush →
leader of the office of scientific research and development, part of
, promoting large-scale penicillin & Manhattan project. They saw
science as a way to win war.
- Innovation → bring technology to commercial use → show that it has
value (you can earn market share, money, advance society),
Schumpeter: invention and innovation; no interactions
- from 1970s people became more critical towards technologies that
entered the market → social construction of technology
- from 1990s onwards, transition studies → how to come to a particular
point (make innovations in such a way that it would lead to the ideal state
→ how to make society more sustainable), more or less directionality of
innovation → innovation for particular societal challenges, speaks to policy
making as well
Lecture 2
Neo-Schumpeterian Economics
Adam Smith (Orthodox economics)
- wealth of nations, law of invisible hand, following existing recipes
- input (labor, capital) → produce good or service a minimum cost (perfect
competition → all kinds of companies are producing at lowest cost
possible)
- only role of governments: avoid monopolies → one company has access to
all resources (impossible for other companies to do it as cheap as you do)
- innovation is at most byproduct of division of labor (innovation is not really part
of this type of thinking)
→ no intervention is needed because everything is going smoothly (due to
invisible hand)
Orthodox Economics
- rational and perfectly informed decisions about the allocation of resources
(by firms) → no so-called strategic uncertainty
- technology conceptualized in the production function
- technological change is fully exogenous (you cannot explain/influence it)
- the economy strives towards equilibrium under a given state of the art
→ Quantity is dependent on the amount of investment in labor & capital
New Economy ≠ Heterodox Economics as it is not saying things about how
economics view the world
Nelson & Winter (Heterodox Economics)
- economists from Yale University
- working for RAND corporation in the 1960s
, - discontent with policy implications of contemporary economic thinking
- influential in a range of fields and approaches
→ according to them, there were things that were not easily explained through
Orthodox Economics → quasi-evolutionary thinking, changed the way of thinking
in Economics → try to come up with a model that could entail innovation
Figure productivity growth → starting point for them: productivity growth differs
per sector and they wanted to explain why this was happening (as labor and
capital could not explain it sufficiently)
Nelson & Winter
- the productivity growth puzzle; what, then, is sector specific?
- difference not explainable by orthodox economics → R&D investment
decisions cannot be made against a clear set of alternatives (fundamental
uncertainty), R&D intensity and the nature of the R&D process within
sectors (technological regimes/paradigms), sources of R&D spending and
rates of internalizing R&D results (institutional environment)
- In search for a useful theory of innovation
Not the very first one in looking at innovation as a driving force, referred back to
Schumpeter:
- successful economic author
- unsuccessful practitioner
- “creative destruction” and “entrepreneurship”
- “monopolistic rents”, need for IP → otherwise you cannot incentivize
organizations to innovate
→ following new recipes, looking at innovation through the lens of creative
destruction. When you introduce a new technology, you get a temporary
monopoly (you are the only one that can sell it). Others will feel the pain of your
creation → “old” products/sectors outcompeted. Explains output (productivity
growth).
The paradigm shift of Schumpeterian economics
- technological change is a core property of economic systems
- deliberately striven for by economic actors to obtain a competitive edge
- constant disequilibrium (“an economy of creative destruction knows only
one pace - hectic”) → organizations constantly reacting to changes within
environment, economy constantly shifting/fluxing → trying new things to
stay ahead
S-curve → technologies are not developed fluently (there can be discontinuity)
New S-curve → radical innovation
Nelson & Winter ended up with selection environment
