Leibniz-Centre for Agricultural Landscape Research (ZALF) e. V.
CA2Africa Inception Workshop, 2-4 March Nairobi
Session 3
Overview of existing modelling approaches
Adoption Decision Theories
and
Conceptual models of
Innovations Systems
Hycenth Tim Ndah
Johannes Schuler
Sandra Uthes
Peter Zander
Tuesday 2nd of March 2010
Leibniz-Centre for Agricultural Landscape Research (ZALF) e. V.
Content
Clarification of terms and principles of CA
Adoption decision theories
Selected Adoption decision theories
Conceptual Models of Innovation system
Innovation systems approach and CA
Selected conceptual models of innovation systems
Applicability of some selected theories and conceptual
models in Agricultural studies
Perspective (PhD work-Schematic presentation)
CA2Africa; Theories, Conceptual models of Innovation Systems
and challenge of BEFMs in comprehending adoption
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Clarification of Terms and meaning of CA
Theory
In English, it is used as a “concept or scheme”. Psychologist say its all
about human thought and behaviour while scientist say it’s a tested and
testable concept explaining an occurrence
Adoption
Adoption is seen as the first or minimal level of behavioural utilization
(Rogers 2003)
Diffusion
is the process by which an innovation is communicated through certain
channels over time among the members of a social system (Rogers 2003)
Innovation
Any new knowledge introduced into and utilized in an economic or social
process (OECD, 1999)
New products and equipment but also new methods and ideas (Hoffmann,
2005)
Innovation system
defined as a network of organizations, enterprises, and individuals focused
on bringing new products, new processes, and new forms of organisation
into economic use, together with the institutions and policies that affect their
behavior and performance (The World Bank 2007)
Agents
comprising individuals and firms as well as public institutions and nonstate
actors (constitute the main operating components of the System)
CA
Calgeri and Ashburner, 2006
N/B: A good functional Innovation System creats an enabling environment
for adoption
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Adoption Decision theories
Categories
Behavioral Theories
Cognitive Theories
Development Theories
Humanist Theories
Personality Theories
Adoption Decision
Theories in
Agriculture
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Cognitive Theories
Action is triggered through the uncomfortable
tension which comes from holding two conflicting
thoughts in the mind at the same time
•
•
•
•
Focus on internal state such as:
motivation
problem solving
decision making
thinking and attention
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Behavioural Theories
Learning based on the idea that all
behaviour is acquired through…….
…………… “conditioning”
used in therapeutic settings to help clients
learn knew skills and behaviours
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Why Adoption Theories in Agriculture ?
Because of focus of economic models on interest
and profit max ("Economic men and women“
Because economic models fail to conceptualize
the social dimensions of knowledge, information,
communication and rationality (Leeuwis,1993)
Because of limited ability of economic models to
explain decision and to capture complexity of
farmers attitudes and behaviour
N/B: Adoption theories therefore try to fill this gaps
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Selected “Adoption decision”
theories
Theory of Psychological Field (Kurt Lewin)
Theory of Behaviour Modification (Albrecht et al 1987)
Hohenheim diffusion Theory (Hoffmann 2006)
Diffusion of Innovation Theory (Rogers 2003)
The Theory of planned Behaviour (Ajzen 1991)
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Theory of psychological field
(Kurt LEWIN)
route
Individual
Person
(farmer)
target
Subjectively perceived
environment
barrier
b = f (P, Esubj. )
Where;
behaviour (b) is a function
of the individuals
subjectively perceived
environment (P,Esubj.)
Adapted from Albrecht et al. 1987 after Lewin, In: Hoffmann, 2005
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Theory of psychological field
(Kurt LEWIN)
Human behaviour is seen as a result of the interplay of diverse forces that create a
set of circumstances through the dynamic interaction of man and his environment
(Albrecht et al. 1987 in; Hoffmann, 2005; Ndah, 2008)
According to the psychological Field theory of Kurt LEWIN, the interaction of
situational forces with the perceived environment can be described as a field of
forces, a system in tension or a psychological field.
Human behaviour can be described as follows: A person (P) in his subjectively
perceived environment feels something is worth striving for (a target e.g CA). He/she
then mobilizes his/her personal powers to achieve this goal (adopt CA)
When something negative or undesirable occurs, he activates his personal powers in
the same way to avoid the negative situation.
Ways of reaching targets and avoiding negative situations can be blocked or impeded
by barriers or inhibiting forces (lack of knowledge, uncertainty about outcome,
insufficient capital, cultural practices, lack of opportunities for scaling up of CA
innovation etc)
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Theory of Behaviour
modification (Albrecht et al 1987)
Phase
1
Phase
2
Phase
3
Behaviour at
different times
Inhibiting
forces
Driving forces
Disturbance of
former
equilibrium
Shift to new
equilibrium
Perception of
problem
Stages of
implementation
Albrecht et al. 1987
Stabilisation of
modified
behaviour
Solution to
problem or
relapse
time
CB=+DF-IF
where:
CB=Change in Behaviour
DF=Driving Forces
IF=Inhibiting Forces
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Theory of Behaviour
modification (Albrecht et al 1987)
•
Inhibiting forces-forces negatively influencing behavioural change
(adoption of CA)
e.g lack of subsidies, limited liquidity (for labour hiring, buying
herbicide, seeds of legumes for soil coverage, etc), lack of machinery,
and limited knowledge
•
driving forces-forces conducive to positive target (adoption)
e.g. financial assistance, technical advice, training, provision of inputs,
financial assistance, linkage with market outlets, etc
•
Behaviour (adoption) is thus seen as resulting from the psychological
field of inhibiting and driving forces
hence these forces are present in a state of equilibrium or disequilibrium with varying degrees of tension between them
•
Once such forces are identified in the farmers decision making
process, the chances of diffusion can be estimated and consequences
for promotion programs can be concluded (Kriesemer and Grötz 2008).
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Diffusion of Innovation
Theory (Rogers 2003)
“audience segmentation”
Number of
adopters per
unit of time
1) Innovators-Venturesome,
educated
2) Early adopters-Social
leaders, popular, educated
1
3) Early majority-deliberate,
many informal social
contacts
2
3
Rogers, 2003
An innovation is :
•
an idea,
•
practice, or
•
object
perceived as new by an
individual or other units of adoption
4
5
Time
4) Late majority-sceptical,
5) Laggards- traditional,
lower social economic class
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The Diffusion Theory
(Hohenheim Concept :Hoffmann 2005)
Diffusion- “process by which an innovation is communicated through certain
channels over time among members of a social system”
(Rogers 2003)
“phases of diffusion”
Number of
adopters per
unit of time
1 The innovator as disruptive
element
2 The critical phase (end or turning
point)
3 Transition to the self-sustaining
process
1
2
3
4
4 Final phase of the wave
Time
Hoffmann, 2005
Leibniz-Centre for Agricultural Landscape Research (ZALF) e. V.
Determinants of adoption
(Rogers 2003)
Independent variables
Dependent Variable
Attributes of innovation: relative
advantage, compatibility, complexity, Trial
ability, observability.
Innovation decision: Optional, collective,
Authority
Rate of adoption of
innovation (CA)
Communication Channels: mass media or
interpersonal
Social system: norms, degree of network
connection
Extent of change Agents Promotion efforts
Adapted from Rogers, 2003
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Determinants of adoption
(Rogers 2003)
Perceived attributes
•
Comparative advantage -the degree to which an innovation is perceived better than the idea it
supersedes.
•
Complexity - the degree to which a practice is perceived as relatively difficult to understand and to adopt.
negatively related to its rate of adoption
•
Trialability -degree to which an innovation (CA) may be experimented at a limited basis.
•
Compatibility-degree to which sustainable practice is perceived as consistent with the existing values, past
experience and needs of potential adopters.
Type of innovation decision
proces through which an individual passes from; knowledge to attitude and finally to adopting (indivual or
collective, optional or authority)
Communication Channels
interpersonal or mass media,
originating from specific or diverse sources
Social system: norms, network interconnectedness
socio-cultural practices and norms that can inhibit or drive adoption
Efforts of promotion agent
past and present efforts made to promote CA (national, international bodies)
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Theory of Planned Behaviour
(TPB) Ajzen, 1991
Likely outcome of behaviour (adop)
e.g Adoption
Expectation of others
Fascilatting & impeding factors
Adapted from Ajzen, 1991
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Theory of planned Behaviour
TpB (Ajzen 1988, 1991)
The theory helps to understand how people’s (adoption decision ) behaviour can be
influenced.
It predicts deliberate behaviour, since behaviour can be deliberate and planned.
Theory assumes human action to be guided by three kinds of considerations:
Behavioural Beliefs (beliefs about the likely consequences of the behaviour-adoption)
Normative Beliefs (beliefs about the normative expectations of others)
Control Beliefs (beliefs about the presence of factors that may facilitate or impede
performance of the behaviour-adoption).
N/B: Ajzen's three considerations are crucial in circumstances such as projects (e.g
CA2Affrica) when analysing peoples behaviour or attitude towards a practice (e.g CA)
http://www-unix.oit.umass.edu/~aizen/tpb.html
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Conceptual Models of Innovation
System
The interest of an innovation systems approach applied to the
understanding of CA development is that it allows to identify
…….which stakeholders are lacking (diagnostic)? or
……..may be needed (recommendation)? ……
…… in the CA development process to overcome bottlenecks and
constraints and generate the needed knowledge, technologies or
institutional arrangements.
Various conceptual model of local innovation systems can be used as
frameworks for analysing the quantity and quality of the flows of information
(exchanges of knowledge, training processes) and…..
…......the decision processes (technical adaptations) between the main
actors.
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Selected Conceptual models of
Innovation systems
Innovation Systems model (The World Bank 2006)
The Innovation Policy Terrain-a map of issues (OECD 1997)
Elements of National Innovative Capacity (Speirs et al 2008 based
on Porter and Stern 2002)
A Generic National Innovation System (OECD, 2003)
A simple innovation network, from Wall et al., 2002, based on
Rycroft and Kash, 1994
Innovation system perspective (Lundvall 1985)
expanded model for adoption of conservation practices (Clearfield
and Osgood, 1986)
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A stylized Innovation System
(The World Bank, 2006)
Sanitory and phytosanitory
standards
Increased
International
Investment
&
Knowledge flows
Licensing
Interactions
Global
Concentration
DNA
Genotyping
Agricultural
Policies
Adapted from : Lynn k. Mytelka, Local Systems of Innovation in a Globalised Economy“ in industriy and Innovation, Vol. 7, 2000,
Cited in : the World bank 2006
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Relating the stylized Innovation System
to the case of CA2Africa
CA Innovation System:
All CA actors (Farmers, experts, machine developers,
Input suppliers, policy makers etc)and….
their linkages (interactions) involved in the
production and use of (CA) knowledge and….
the rules and mechanisms –institutional and policy
context that shapes the processes of (CA) knowledge
…………….access, sharing and learning.
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The Innovation Policy Terrain (IPT)-a
map of issues (OECD,1997)
broader framework conditions (region, district)
•
legal, economic, financial, and educational setting
•
rules and range of opportunities for innovation;
•
science and engineering base (accumulated
knowledge and the science and technology)
•
institutions ( technological training and scientific
knowledge)
•
transfer factors (region, district)
Human, social and Cultural factors: influenceing the
effectiveness of the linkages, flows of information
and skills to firms and learning by them
OECD, 1997
innovation dynamo
•
is the domain most central to business innovation
it covers dynamic factors
•
within or immediately external to the firm and very
directly impinging on its innovativeness.
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Elements of National Innovative Capacity (ENIC) model
(Porter and Stern 2002 as cited in: Speirs et al. 2008)
The Common Innovation Infrastructure (region, district)
set of human, financial, public policies, economy’s level of technological
sophistication, environment within which all innovating enterprises must
operate.
Porter and Stern 2002
Cluster-Specific Conditions (region, district)
defined as a: “….geographic concentration of interconnected companies and
institutions in a particular field.”
–
further viewed as four interrelating attributes, each contributing to the
innovative capacity of the ‘cluster’.
–
the context for firm strategy and rivalry,
–
factor or input conditions (human capital, risk capital, research
infrastructure and information infrastructure),
–
demand conditions (insight gained from sophisticated local demand)
related supporting industries (local suppliers, related companies and
the presence of these in localised industries or ‘clusters)
Quality of Linkages (region, district)
relationship between the common infrastructure and a nation’s industrial
clusters.
described as reciprocal as clusters are said to be able to feed and benefit
the common infrastructure.
relationship is governed by formal or informal organisations that facilitate the
links between the common innovation infrastructure and industrial clusters
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A Generic National Innovation System (NIC)
model (OECD, 2003)
OECD, 2003
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A simple innovation network, (from Wall et al., 2002,
based on Rycroft and Kash, 1994.
Machinery
Manufacturers
Input
Suppliers
Extension
Service
Innovative
Farmers
Research
Equipment
Developers
A way to simulate these systems is through multi-agent based
modelling or fuzzy-cognitive mapping.
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Expanded model for adoption of conservation
practices (Clearfield and Osgood, 1986)
Expanded model
potrays a balanced
presentation of
socio-psychological,
farm structural,
ecological,
institutional.
As broad categories
of explanatory
variable to adoption
(CA)
Groups of explantory variables for adopting
Conservation practices
Farm structure
Socio-Psychological
Ecological
Institutional
Adapted from Clearfield and Osgood, 1986
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Selected applicability of theories, and
Conceptual models
Wauters, E. 2005, Combines TPB (Ajzen 1991) with linear regression techniques to examine the adoption of
cover crops, reduced tillage and buffer strips in Belgium
Kriesemer K.S., Grötz P.A. 2007 combines the theories of Innovation diffusion by Rogers 2003, Behaviour
modification by Albrecht et al 1987 and the Hohenhein diffusion concept by Hoffmann 2005a to examine the
adoption and diffusion of Small-Scale Aquaculture in Africa with special reference to Malawi
Ndah H. 2006, uses the behaviour modification Theory along side variables of adoption (Rogers 2003) as a
framework to examine the driving and inhibiting forces to Fish Pond Aquaculture in Cameroon
Sattler, C.; Nagel, U.J., N 2003 uses the attributes of Innovation suggested by Rogers 2003 to examine the
factors affecting farmers´ acceptance of conservation measures in north eastern Germany
Padel, C. 2001 uses the diffusion of Innovation Model (Rogers 1983) in his work; Conversion to organic farming:
A Typical Example of the Diffusion of an Innovation?
Speirs J., Pearson P., Foxon T.,(2008) in the study: Adapting Innovation Systems Indicators to assess EcoInnovation analysed strands of literature in the four conceptual models
•
•
•
•
Innovation Policy Terrain (OECD 1997);
Generic National Innovation system Model ( OECD 2003)
Elements of NIC model (Porter et al. 2002) and
Functions of Innovation sysytem model (Jacobsson & Bergek 2004; Hekkert et al. 2007).
To develop sets of indicators or guidance for the measurement of eco-innovation
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Perspective
CA2Africa; Theories, Conceptual models of Innovation Systems and challenge of
BEFMs in comprehending adoption
Broad screening and selection of best fit conceptual models and adoption theories
Best selected and reviewed Conceptual models, theories, and indicative areas
or guidance for capturing systems indicators at linkages, institutions,
and frameconditions levels (review)
BEFMs as Computer
base technological
Innovation (tool)
Pre-modelling
modelling and Post
modelling
CA innovation as a
practice
Adapting Innovation systems indicators to measure CA adoptive an
innovative capacity with contributions and suggestions- from all
actors in the system
District, Regional, and international actors and institutions; their linkages, interractions and rules
in the CA innovation system. Diagnostic and verifying phase through integrated methodology: selected structured, Semi structure qualitative
interviews, focus groups, key informants and Expert interviews
1) Reseachers 2) extension workers and rural sciologist 3) Innovative farmers, 4) policy makers 5) input supliers 6) NGOs,
7) farmers groups 8) CA machine manufacturers and developers,
Leibniz-Centre for Agricultural Landscape Research (ZALF) e. V.
Perspective
CA2Africa; Theories, Conceptual models of Innovation Systems and challenge of
BEFMs in comprehending adoption
Broad screening and selection of best fit conceptual models and adoption theories
Best selected and reviewed Conceptual models, theories, and indicative areas
or guidance for capturing systems indicators at linkages, institutions,
and frameconditions levels (review)
BEFMs as Computer base
technological Innovation (tool)
Best selected BEFMs,
possible integration of adapted
indicators to measure CA
Strenghts and weaknesses of
BEFMs and possible contribution of
extensionist, Rural sociologist in
improving the capacity of such
models to capture and
conceptualise
the social milieu limitations towards
comprehending adoption of CA
Pre-modelling
modelling and Post modelling
attempts to capture the social
dimensions of knowledge,
information,communication
and rationality how this helps
in comprehending adoption,
further development of the
model, policy
recommendation,
contribution to DSS
CA innovation as a practice
CA meaning, techniques its
variables and attributes to
Adoption as an innovation in
Practrice (Compartibility
Trialability Comparative
adavantage,Observability.
Regional firness to various agro
ecological zones of Africa under
the various socio economic and
and cultural conditions
Adapting Innovation systems indicators to measure CA adoptive an
innovative capacity with contributions and suggestions- from all
actors in the system
District, Regional, and international actors and institutions; their linkages, interractions and rules
in the CA innovation system. Diagnostic and verifying phase through integrated methodology: selected structured, Semi structure qualitative
interviews, focus groups, key informants and Expert interviews
1) Reseachers 2) extension workers and rural sciologist 3) Innovative farmers, 4) policy makers 5) input supliers 6) NGOs,
7) farmers groups 8) CA machine manufacturers and developers,
Leibniz-Centre for Agricultural Landscape Research (ZALF) e. V.
References
•
Rogers, E. M. (2003) Diffusion of innovations, fifth edition. Free Press, New York, U.S.A.
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Hoffmann, V. (2006) Knowledge and Innovation Management, Reader, University of Hohenheim, Stuttgart, Germany.
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Hoffmann, V. (2005) Rural Communication and Extension, Reader, University of Hohenheim, Stuttgart Germany
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Sattler, C.; Uwe Jens, N. (2004) Factors affecting farmers‘ acceptance measures; Leibniz Centre for Agricultural landscape Research (Zalf) e.V., Institute of
Socio Economics; Humboldt University of Berlin, Faculty of Agriculture and Horticulture.
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Kriesemer, S. K.; Grötz, A. (2008) The Adoption and Diffusion of Small-Scale Pond-Aquaculture in Africa with special Reference to Malawi
•
Ndah, H.T. (2008) Adoption and Diffusion of Fish Pond Aquaculture in Cameroon; An empirical study carried out in the Centre, Southwest and Northwest
Provinces of Cameroon
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Hess, S.C. (2007) Customers´ decision Making within Innovation Adoption Process-Understanding Customers´Adoption Behaviour and Managing Adoption
Barriers
Lundvall, B.Å (1985) Product Innovation and User–Producer Interaction. Aalborg University Press
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Leeuwis, C. (1993) Of Computers, myths and modelling; the social construction of diversty, knowledge, information and communication
Technologies in Dutch horticulture and agricultural extension
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Spielman, J. (2005) Innovation Systems Perspectives on Developing-Country Agriculture:A Critical Review. ISNAR Discussion Paper 2
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Porter, M.; Stern, S. (2002) The global competitiveness report, World Economic Forum, Geneva, Switzerland (2001), New York,. Oxford
University Press: 102-118. Remøe, S. (2005) cited in Jamie Speirs S., Pearson P., Foxon T. (2008) Adapting Innovation Systems Indicators to
assess Eco-Innovation
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European Commission (1997) The OSLO MANUAL: The measurement of Scientific and technological activities; Proposed guidelines for
collecting and interpreting technological innovation data
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Wauters, E. (2005) Adoption of soil conservation measures in Belgium: applying the theory of planned behaviour
•
World Bank (2007) Enhancing Agricultural Innovation: How to go beyond the strengthening of research systems. Washington DC, USA: World
bank
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Fischer, A. J.; Arnold, A.J.; Gibbs, M. (1996) Information and the Speed of Innovation Adoption
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