Rural Economy Research Centre Understanding farmers’ intentions to convert to organic farming An...

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

Rural Economy Research Centre2

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?


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)


Theory of planned behaviour

Source: Ajzen, 2005.


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)


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


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


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)


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


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***


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***


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)


x2

x3

x1

y1 y2 y3y4

y5

y6

)(

Model


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


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


Thank you for your attention!

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Rural Economy Research Centre Understanding farmers’ intentions to convert to organic farming An...

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