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Rural Economy Research Centre
Understanding farmers’ intentions to convert to organic farming An application of the theory of planned behaviour using structural equation modelling
Doris Läpple
Rural Economy Research Centre (RERC) TeagascDepartment of Economics, National University of Ireland, Galway
AESI student awards day 5th November 2009
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Overview of presentation
Background
Theory
Survey design and data
Methodology
Preliminary results
Summary and further work
Background
Organic sector
Currently 1,220 organic farmers (1% of UAA) Government target: 5% of UAA by 2012
Increased information provision Changes in organic support payments
Conventional sector
Extensive mainly grass based systems many drystock farmers could easily switch to organics
What effects the intention of conventional farmers to convert?
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Background
PhD: Adoption of organic farming Previous paper: Adoption and abandonment of organic farming
Policy context Conversion of existing farmers Identify drivers and barriers
Application of the TPB to understand/predict different human behaviours Hunting behaviour (Hrubes et al, 2001), leisure choice (Ajzen and
Driver, 1992), food choice (Cook et al, 2002; Lobb et al, 2007), investment behaviour (East, 1993), consumer adoption intention (Taylor & Todd, 1995)
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Theory of planned behaviour
Source: Ajzen, 2005.
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Attitude toward the behaviour
Subjective norm
Perceived behavioural
control
Behaviouralbeliefs
Normative beliefs
Control beliefs
Intention Behaviour
Theory of planned behaviour belief based measures
Behavioural belief:
If you produce organic meat you will receive higher prices
strongly agree (5) to strongly disagree (1)
Outcome evaluation:
Receiving higher prices is…
very important (+2) to very unimportant (-2)
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ii ebA
Theory of planned behaviour
Components consist of direct and belief based measures Belief based measures should correlate well with direct measures
of the specific component
→ Salient beliefs
The more positive the attitude, subjective norm and perceived behavioural control the more likely the person is to perform the behaviour under study
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PBCwSNwAwBI 321
Methodologysurvey design
Step 1: Open interviews to elicit salient beliefs (n = 53) Outcome beliefs Important referents Expected problems
Step 2: Design structured questionnaire (n = 193) Principle of compatibility
Target - organic meat Action - producing meat organically Context - the farm Time - 5 years
Data from NFS farms
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Methodologysurvey design Intention (2 items):
How likely is it that you will produce organic meat on your farm within the next five years? measured from very likely (5) to very unlikely (1)
Attitude (3 items):
Producing organic meat on your farm within the next five years would be… very good (+2) to very bad (-2) ; very foolish (-2) to very wise (+2)
SN (2 items):
Most people who are important to you think you should produce organic meat on your farm within the next five years…
definately false (-2) to definately true (+2)
PBC (2 items):
How confident are you of your technical ability to produce organic meat on your farm within the next five years?
not at all (-2) to very confident (+2)
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Resultsdescriptive statistics
Variable Range Mean St. dev. Cronbach‘s α
(n=193)
Intention 1 to 5 1.907 0.966 0.877
Attitude -2 to +2 -0.055 0.763 0.870
SN -2 to +2 -0.521 0.746 0.561
PBC -2 to +2 -0.238 0.923 0.487
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Resultsvalidation of belief based measures
Behavioural belief bi oei rs A
Range 1-5 -2/+2 (-10/+10)
Fertilizer 4.123 (0.767) 1.082 (0.799) 0.069
Higher prices 3.456 (0.935) 1.290 (0.776) 0.213**
Farm income 3.197 (0.868) 1.275 (0.738) 0.337***
50 ys ago 3.196 (1.091) -0.762 (0.869) 0.412***
Rich people 3.321 (0.995) -1.010 (0.685) 0.341***
Σ b2 b3 0.305***
Σ b4 b5 0.402***
Σ b2 – b5 0.492***
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Resultsvalidation of belief based measures
Normative belief
nbi mci rs SN
Range -2 /+2 1-5 -10 / +10
Family -1.129 (.962) 2.477 (1.335) 0.354***
Other farmers -1.072 (.971) 2.104 (1.010) 0.364***
Farm advisors -0.778 (1.019) 2.580 (1.285) 0.366***
Inf. Events -0.699 (1.077) 2.352 (1.267) 0.363***
Farming press -0.710 (1.103) 2.259 (1.231) 0.411***
Σ nb1-nb5 0.490***
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MethodologyStructural Equation Model (Covariance Structure Model)
SEM: Statistical methodology to test a theoretical model Causal inferences of the theory are consistent with the data
population covariance matrix
of the observed variables
vector of model parameters
Two major subsystems: Measurement model (confirmatory factor analysis) Latent variable model (structural model)
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x2
x3
x1
y1 y2 y3y4
y5
y6
)(
Model
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A1
A2
A3
SN1
PBC2
A
SN
PBC
BI
I1
I2
Results Structural equation model
Parameter Estimate (st.err.) t-value
A (11) 0.587 (0.090) 6.517
SN (12) 0.282 (0.095) 2.953
PBC (13) 0.169 (0.057) 2.979
I1 (11 ) 0.872 (0.034) 25.901
I2 (12) 1.000 (---)
A1 (23) 0.867 (0.039) 22.061
A2 (23) 1.000 (---)
A3 (23) 0.846 (0.052) 16.235
2 =6.77, df=10, P-value = 0.747, 2/df= 0.677
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Summary and further work
Belief measures significant determinants of direct measures Confirms model
Direct components are be significant indicators of intention Attitude strongest predictor PBC – lower value explained by measure (self-efficacy)
Further work: Improve modelling Include belief based measures
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Thank you for your attention!
Rural Economy Research Centre