Why behavioural science m= atters in extension
J Pickering, A Jenner, K Haanterä, S Moore, C Iseppi, B Markey-= Towler & N Ruzsicska
Evidn Pty L= td, 315 Brunswick St, Fortitude Valley. Qld 4006.
Email: john@evidn.com
Abstract. = This paper demonstrates the applicability of behavioural science to agricultural extension and the value of behavioural science training to enhance extension practice. Examples of how behavioural science is applicable to extension we= re explicated, namely: that farmers’ attitudes and behaviours are influenced by their social groups; practice change can be adversely impacted by errors in= natural decision-making processes; why people may not be inclined to change; and th= at farm management decisions are influenced by a system of factors beyond the individual. A behavioural science training model was developed and delivere= d in 2019 to 57 extension professionals from a range of agricultural sectors in = five locations across Queensland. Results indicate that after attending the workshop, participants had an improved ability to understand farmers’ psychology and apply behavioural science to overcome resistance to practice change. Future research in this area may endeavour to develop more advanced training modules for experienced extension officers.
Keywords: =
behavioural science, behaviour ch=
ange,
extension, training, adoption
The
Australian agricultural sector is facing substantial pressure to increase t=
he
uptake of sustainable farming practices due to rising public concern about =
the impact
of farming on the environment. One of the key roles for extension officers =
in the
effort to increase the uptake of sustainable farming practices is to keep
farmers up to date with developments in agricultural science to facilitate
practice change (Oakley & Garforth 1985;
Department of Environment and Science 2015). As part of their role, extensi=
on
officers are required to listen to the wants and needs of farmers, and be a
resource to further develop their knowledge and skills - empowering them to
respond to challenges and opportunities (Gladwin et al. 2002; Coutts et al.
2005). It is through extension officers’ engagement with individuals, groups, and organisatio=
ns in
the agricultural sector, that they help to generate and disseminate innovat=
ion
and practice change (Rickards et al. 2019).
The context in which extension offic= ers operate is constantly evolving. Agricultural extension practice must adapt = to ongoing updates to scientific knowledge, R&D, as well as the stakeholde= rs and agents that influence farmer decisions (Rickards et al. 2019). Given the evolving nature of extension, there is question as to whether the current state of extension training has also evolved to sufficiently address the diversity of skills, knowledge, and attitudes needed for the role (Ragasa et al. 2016; Landini et al. 2017).
There is no universally agr= eed upon set of skills that extension officers need in order to<= /span> do their job effectively, with research indicating that the skills needed a= re likely to depend on the idiosyncrasies of their region and the objectives of the organisation they work for (Lindner et al. 2003; Wanjiku et al. 2010; <= span class=3DSpellE>Landini & Brites 2018= ). Current training models tend to have a strong focus on the technical aspects of extension (Ragasa et al. 2016), yet there is a consensus that extension training must incorporate both technical and psych= osocial components (Tarekegne et al. 2017; Landini & Brites 2018= ).
The psychosocial component =
of
extension is an important, but often overlooked, area for skill development=
. Extension
officers are required to carry out various social processes, including
relationship and consensus building, mediation and conflict management,
coordination of groups, interpersonal and intercultural communication, and
horizontal teaching and learning (Leeuwis &=
van den Ban 2004; Boas
& Goldley 2005; Khalil et al. 2009; Landini et al. 2017; Pickering et al. 2019a). The benefit=
s of
having strong interpersonal skills are important beyond agricultural extens=
ion
– they are a critical competency for the modern workforce (Bedwell et al.
2014). Given that psychosocial, human-centred skills and abilities =
are
a core component of extension practice, it is imperative that extension
training is in line with the requirements of the role.
The field of behavioural sc= ience has been a useful inclusion in designing and delivering training that meets the needs of extension officers. Behavioural science is the science of human decision making and behaviour, and draws upon a range of interconnected fie= lds including psychology, sociology, behavioural economics<= /span> and marketing (Colman 2015). Tertiary studies in agricultural extension in Queensland have incorporated elements of social psychology, sociology, and anthropology into extension training, since 1963 (Tully 1964). The field has also previously been utilised in agriculture and extension to understand farmers= ’ attitudes, perceptions, knowledge, values, and more broadly, the psychological factors associated with the adoption of farming practices and innovation (Tully 196= 7, cited in Murray-Prior et al. 2006; Pickering et al. 2018). The incorporatio= n of behavioural science into extension training can help extension officers understand the broad range of factors impacting farmers’ decision making and behaviour, and equip them with the skills to enhance the quality of their r= elationships and psychosocial processes.
The following section of th= is paper outlines four examples of how behavioural science can help extension office= rs understand the decision making and behaviour of farmers, particularly as it pertains to practice change.
Various
sociopsychological theories can enhance extension officers’ understanding of
how farmers may be influenced by the groups around them. The Social Identity Approach posits that
people belong to a range of different social groups (e.g. family unit,
occupation, geographic location, social class) and that these groups influe=
nce
our sense of who we are, and shape our beliefs and behaviours (Tajfel &
Turner 1979; Turner et al. 19=
87). The social groups we identify with car=
ry
with them an emotional significance, fuelling our self-esteem, sense of mea=
ning
and general wellbeing (Jetten et al. 2017). It =
is no
surprise then, that people
act in ways that are compatible with their social identity; in ways that th=
ey
consider normal for ‘someone like me’ (Sparkman & Walton 2019). =
The large influence that a farmer's in-group (e.g. their farming
community) has on their attitudes and beliefs about a certain subject or
practice has long been observed (Tully 1964). More recently, research with
Australian farmers found
that identification with the in-group and perceptions of the what is ‘norma=
l’
for the group (i.e. group norms) influenced farmers’ intentions to adopt new
farming practices (Fielding et al. 2008).
The Social Identity Approach describes how people look to their group for cues on how to think and behave (Terry et al. 1999). Social Cognitive Theory adds to this by demonstrating how people learn new behaviours based = on observing the behaviour of other people in their social setting (Bandura &a= mp; Walters 1977). Social Cognitive Theory states that individuals commonly obs= erve the behaviour of those in their social group and use that observation to formulate their own ideas and beliefs about the behaviour and ultimately ‘model’ (i.e. copy) it. Interaction between members of a farmer group (i.e.= the in-group), facilitates the transmission of values, attitudes, and beliefs between members, leading to the uptake of new behaviours, and eventually the emergence of a new norm within the farming community (Tully 1964).
Social Identity Approach an=
d Social
Cognitive Theory provide the theoretical basis for this phenomenon, explain=
ing how
certain behaviours come to be shared among peer groups or are evident in
certain regions (and not in others). They explain how people look to the groups they belong to for cues on=
how
to think and behave, and then observe and model the behaviours of group
members. Behavioural science can enhance extension practice by explicating =
why
farmer interaction within peer networks should be encouraged, and how
shared social identities can be utilised and leveraged to bring about behav=
iour
change.
Decision making processes a=
re thought
to be underpinned by two modes of thought: System 1 and System 2 (Kahneman
2011). System 1 thinking is fast,
automatic, habitual, and unconscious. System 2 thinking, contrarily, is slo=
w,
deliberate, effortful, and conscious. Most decisions we make daily use Syst=
em 1
because it is much more efficient than System 2 (Simon 1955). In System 1, =
we
can make quick decisions by relying on mental shortcuts (i.e. rules-of-thum=
b).
Hierarchical decision modelling is a related, yet distinct two-part
decision-making process that has been used to understand and predict farmer
decision making, such as whether to adopt a new practice (Gladwin 1977, 198=
3).
The first stage in the decision-making process refers to 'unconscious
processing', akin to System 1 thinking. This stage involves comparing the
behaviour against a set of criteria in a heuristic-like manner. Such criter=
ia
could relate to whether the farmer has the capital, resources, and time to
adopt the new practice. After considering the decision against the preconce=
ived
criteria, if the new practice remains a viable option, the farmer will then
engage in the second stage of the decision-making process, a more 'consciou=
s'
decision process (Murray-Prior 1998). In this phase, the decision is thorou=
ghly
considered, akin to System 2 thinking. Models based on the hierarchical
decision modelling framework have been successful in predicting farmer choi=
ces
(Zabawa 1984; Murray-Prior 1998).
While operating in System 1=
, or in
the first phase of the hierarchical decision-making process, we rely on men=
tal
shortcuts (i.e. rules-of-thumb) to guide our decisions. Although mental
shortcuts can be highly effective for making quick judgements and decisions=
, they are not infalli=
ble
(Gigerenzer & Todd 1999). Mental shortcuts =
rely
on generalisations which leads us to miss important information, thereby
introducing bias and error into our judgements.
Cognitive biases can be a major barrier against practice chan= ge. For example, hierarchical decision modelling suggests that when faced with = a decision about whether or not to adopt a certain practice, if the farmer firstly compares the practice against a heuristic that is flawed or outdated, this = may hinder them from then engaging in any deeper consideration of the decision (i.e. System 2 thinking) (Murray-Prior & Wright 2001). Importantly, extension officers are not immune to the influence of biases. It is just as easy for a farmer to overestimate the validity of their thinking based on decades of practice as it is a technical professional to overestimate the validity of their thinking about what practice a farmer ought to adopt. Thu= s, building an understanding of decision-making processes and the potential bi= ases that may arise in the judgements of both farmers and extension officers can= be a useful tool for enhancing extension practice. Table 1 provides a few exam= ples of cognitive biases typical to a practice change context.
Table 1. Example
biases typical to a practice change context
|
Type of Bias |
Definition |
Example |
|
Overconfidence Bi= as |
The tendency to systematically overestimat= e our judgements to be more reliable than what they objectively are (Kruger &am= p; Dunning 1999). |
A farmer may be overconfident in their judge= ment of the productivity benefits of a farming practice. |
|
Confirmation Bias= |
The tendency to seek out and give more consideration to information that confirms our pre-existing beliefs, and ignore information that does not (Wason 1960; Nickerson 1998). |
A farmer may believe that a certain farming practice is more effective than another, and give more weighting to information that confirms their belief, while discounting information that does not. |
|
Availability bias= |
The tendency to judge the likelihood of an= event based on the ease of which related scenarios or instances come to mind (Tversky & Kahneman 1974). |
A farmer may judge the quality of a new piec= e of equipment, or practice, based on the most recent information that they he= ard from a neighbour, ignoring other facts. |
Humans are creatures of habit; we are not inclined to change and often hold the misconception that we do not need to (Samuelson & = Zeckhauser 1988). Research has demonstrated the tendency for people to disproportionat= ely prefer to stick with their current behaviour, or the status quo, as opposed to change their behaviour when faced with conflicting alternatives = (Samuelson & Zeckhauser 1988). The tendency to stick with current beliefs and behaviour is amplified when the decision to change is complex, has <= /span>various options, and uncertain outcomes (Fleming et al. 2010). People are more inclined to change their behaviour w= hen the alternative is easy to do and relatively risk-free compared to when the alternative behaviour is difficult, and the payoffs are unclear (Kahneman et al. 1991). In the context of agricultural practice change, farmers may be more hesitant to adopt a new practice when there are multiple options to ch= oose from and there is uncertainty around the associated benefits and payoffs.
In addition to the desire to stick with =
the
status quo, people are inclined to use motivated reasoning to justify their
beliefs or behaviours in the face of opposing information. Motivated reason=
ing
is the tendency for people to be emotionally motivated to construct
justifications for their decisions and actions rather than taking a logical
approach to reasoning (Kunda 1990). For example=
, even
when individuals are given the same information about political parties, the
emotionally fuelled motivation for people to see their preferred political
party in a positive light can bias the way that the information is received=
and
processed (Kahan 2012). Motivated reasoning explains how people can ‘reason
away’ contradictory information so that their pre-existing beliefs and
behaviour are justified.
The tendency for people to stick with the
status quo, and process information in a way that maintains their current
beliefs or behaviour are just two examples of psychological processes that
underlie why people don’t think they need to cha=
nge. Behavioural
science can help shed light on the reasons why farmers may wish to retain t=
heir
current behaviour and when they may be more likely to consider alternative
views or options.
A popular extension framework that has b=
een
used to identify the driving and restraining forces to behaviour is the =
Force
Field Analysis (FFA; Lewin 1951), which was developed based on Lewin’s
field theory. The FFA posits that driving forces are those that motivate the
individual to engage in a certain behaviour and restraining forces are those
that inhibit the individual from engaging in the behaviour. The model sugge=
sts
that behaviour is the product of a multitude of driving and restraining for=
ces
which can sit at any level of an individual’s environment, namely the sy=
stem
within which they operate. The resulting, observable behaviour is
represented as the equilibrium of the opposing driving and restraining forc=
es.
The FFA model matters for extension, as it suggests that it would be more
beneficial for extension officers to work alongside farmers to identify the
driving and restraining forces at all levels of the system, and work to red=
uce
the modifiable restraining forces rather than solely emphasising the benefi=
ts
of a new practice.
Field theory and the FFA technique demon=
strate
how necessary it is to consider the broader system of contextual factors wh=
en
aiming to understand and change behaviour.
Havens and Finn (1975, cited in Goss 1979) studied the characteristics of
adopters and non-adopters of new coffee-producing technology in Columbia. T=
hey
found that it was not the personal characteristics of farmers that predicted
whether they adopted the new practice, but rather contextual factors. For m=
ost
of the farmers, adopting the new practice would result in a financial loss =
over
the coming years and thus, financial assistance was required. However, there
was not equal access to financial assistance across the farmer population,
which precluded many farmers from adopting the new technology, even if they
wanted to. In this scenario, the FFA suggests that the reduction of
restraining forces - the inequities in access to financial assistance - is =
the
first step to bringing about change.
This paper has thus far has outlined four
examples of how behavioural science is applicable to extension practice. The
examples of behavioural science in agricultural extension provide insight i=
nto
the content and approach taken when creating behavioural science extension
training. The remainder of the paper outlines the results of trialling a
behavioural science-based training model which aims to enhance the skillset=
of
extension officers to engage with farmers and bring about change on-farm.
The following section reports on the results of trialling a behaviou=
ral
science extension training package, delivered in the form of a roadshow acr=
oss
Queensland in 2019. The discussion updates and extends on findings presente=
d in
earlier work providing training for 18 extension officers (see Pickering et=
al.
2019a).
In 2019, the Queensland Department of Agriculture and Fisheries
commissioned Evidn (then Behaviour Innovation) =
to
provide behavioural science training for extension officers operating in all
major Queensland agricultural sectors (cane, grazing, horticulture). This w=
as
done in five locations across the state (Biggend=
en,
Rockhampton, Innisfail, Ingham, Townsville) for 57 professionals.
The
training content was developed by firstly consulting extension officers to
identify skill gaps and opportunities for development, before drawing on re=
levant
psychological and behavioural science literature and learnings from the
company’s flagship project, Project Cane Changer (Pickering et al. 2019b). A
full overview of the methodology for developing the training content, inclu=
ding
how the training was tailored to an extension context and the evidence-base=
for
core modules, is described in a previous paper (Pickering et al. 2019a).
The workshop was delivered over the course of a full working day in =
an
interactive manner that incorporated multimedia and sought to blend
evidence-based theory, case studies and simulations across three modules:
1.&n=
bsp;
=
The
Psychology of Resistance: A
“crash course” in psychological and behavioural science theories that expla=
in
key sources of resistance to practice change, especially in agriculture.
2.&n=
bsp;
A
Toolkit for Change: An
exploration of practical techniques that extension officers can use to enha=
nce
their interactions and relationships with farmers.
3.&n=
bsp;
=
Putting
it all into practice: A
consolidation and application of the prior modules by use of a case study, =
as
well as an exercise creating a personal implementation plan.
A range of quantitative and qualitative feedback was obtained throug=
h a
survey that was administered before and after the workshop. It comprised th=
ree
questions which were evaluated using a 7-point Likert scale ranging from 1
(very poor) to 7 (excellent). The quantitative results of the survey are
reported in Table 2.
Table 2. Pre- =
and
post-workshop mean scores* and standard deviations from workshop attendees’
responses (averaged across the five locations)
|
Evaluation survey
question |
Mean (SD) pre-wor=
kshop
score |
Mean (SD) post-wo=
rkshop
score |
|
Q1. How would you rate your understanding of the psychological
reasons behind farmers’ resistance to change? |
3.93 (0.23) |
<=
span
style=3D'mso-fareast-font-family:Verdana'>5.31 (0.22) |
|
Q2. How would you rate your understanding of the cognitive and
behavioural skills used to enhance farmer engagement? |
3.45 (0.30) |
<=
span
style=3D'mso-fareast-font-family:Verdana'>5.14 (0.20) |
|
Q3. How would you rate your ability to apply behavioural science
with farmers? |
3.43 (0.29) |
<=
span
style=3D'mso-fareast-font-family:Verdana'>4.90 (0.24) |
* 7-= point score from 1 to 7 with 7 being excellent
Due to the naturalistic set= ting of the training, it was deemed inappropriate to test for statistical significa= nce of the differences in self-assessments before and after the training. Overa= ll, there was an increase across all three evaluation criteria, such that after attending the training, attendees felt they had:
1.
2.
3.
Quali= tative data collected by telephone interviews and survey comments aligned with the positive quantitative results. The table below displays examples of attendee feedback.
Table 3. Post-=
workshop
qualitative feedback
|
Positive |
For improvemen=
t |
|
Great to see the importance of improving the ability to deal wit=
h the
human element being rolled out in training. A great workshop with very valuable tools and considerations when
working with growers as well as friends, family, and colleagues. A good overview of growers’ engagement – enjoyed the references =
to
underlying theory. |
Would
have liked the training to be set at a higher level – risk people switchi=
ng
off as they feel they know it. Good
workshop, but pitched as [sic] a low level with many experienced
extension officers in the room. Was
familiar with much of the content, but enjoyed revisiting it. |
The qualitative feedback in= dicates that overall, extension officers felt that the training was useful in supplementing their technical knowledge of agricultural practice. Several suggestions for improvement centred around accessing higher level behaviour= al science training, indicating that the development of more advanced training modules may be valuable for experienced extension officers. .
Overall, the results of the training support the utility of behavioural science in enhancing extension outcomes. In 2018 a behavioural science training package was developed and trialled among extension officers working in the sugarcane sector with posi= tive results (see Pickering et al. 2019a). The current paper extends these findi= ngs, by scaling the training model and transferring it to other agricultural sectors. The success of this initiative indicates that behavioural science training is applicable and useful for extension officers working across all sectors. This is unsurprising, given that the interpersonal, human-related skills are a core component of extension practice regardless of farming sec= tor. Future research may endeavour to develop more advanced training modules for experienced extension officers or test the effectiveness of behavioural sci= ence training embedded into the ongoing professional development of extension officers.
The behavioural science component of agricultural extension is a k= ey requirement for bringing about practice change on-farm. There is some evidence that current extension training models do not adequately address t= he psychosocial aspects of extension practice. Behavioural science training of= fers a solution to building the psychosocial capacities of extension officers.
Behavioural science is the = science of decision making and behaviour, and when applied to agriculture, can offer extension officers a deeper understanding of farmer psychology. This paper identified ways that behavioural science can enhance extension outcomes, namely: that farmers’ attitudes and behaviours are influenced by the social groups they belong to; practice change can be adversely impacted by errors = in decision making processes by farmers and extension officers; people have a tendency to stick to the status quo; and that farmer decision making is influenced by a system of factors beyond the individual, which need to be identified and understood when bringing about change on-farm.
To demonstrate the efficacy= of behavioural science in agricultural extension, this paper reported on the results of trialling an extension training model incorporating behavioural science. The training was delivered to 57 extension officers working across= all major agricultural sectors in Queensland. Results indicate that overall, the training increased extension officers’ ability to understand farmer psychol= ogy as it relates to practice change and apply behavioural science strategies in the field with farmers to accelerate adoption efforts.
At its core, behavioural sc= ience is a useful tool for understanding human behaviour and facilitating behavioural change. Behavioural science training has the potential to improve the psychosocial skills and abilities of extension officers, and thereby improve the quality of their relationships and their practice. It is hoped that behavioural science can make an ongoing contribution to solving complex problems in the extension sector and agriculture more broadly.
The Behavioural Science Ext= ension Training Roadshow (2019) was funded by the Queensland Department of Agricul= ture and Fisheries and was designed, delivered, and evaluated by Evidn (formerly Behaviour Innovation).
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Rural Extension & Innovation Systems Journal, 2020= 16(1) – Practice © Copyright APEN
Rural Extension & Innovation Systems Journal, 2020= 16(1) – Practice © Copyright APEN