Understanding the biosecurity monitoring and reporting intentions of livestock producers: Identifying opportunities for behaviour change

Accepted Manuscript Title: Understanding the Biosecurity Monitoring and Reporting Intentions of Livestock Producers: Identifying Opportunities for Behaviour Change Authors: Breanna K. Wright, Bradley S. Jorgensen, Liam D.G. Smith PII: DOI: Reference:

S0167-5877(18)30221-6 https://doi.org/10.1016/j.prevetmed.2018.07.007 PREVET 4499

To appear in:

PREVET

Received date: Revised date: Accepted date:

26-3-2018 5-7-2018 5-7-2018

Please cite this article as: Wright BK, Jorgensen BS, Smith LDG, Understanding the Biosecurity Monitoring and Reporting Intentions of Livestock Producers: Identifying Opportunities for Behaviour Change, Preventive Veterinary Medicine (2018), https://doi.org/10.1016/j.prevetmed.2018.07.007 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Understanding the Biosecurity Monitoring and Reporting Intentions of Livestock Producers: Identifying Opportunities for Behaviour Change. RUNNING TITLE: BIOSECURITY MONITORING AND REPORTING BEHAVIOURS Authors: Breanna K. Wright1

IP T

Bradley S. Jorgensen1 Liam D. G. Smith1

1

SC R

Affiliations:

BehaviourWorks Australia, Monash Sustainable Development Institute, Monash University,

U

Australia.

N

Corresponding Author:

A

Breanna Wright

[email protected]

8 Scenic Boulevard

ED

Phone: +61 03 9905 9323

M

Research Fellow

PT

Monash University, Clayton Campus VIC 3800 Australia

CC E

Word count: 6141

Number of Tables: 5

A

Number of Figures: 2

1

IP T

Highlights  Monitoring and reporting behavioural intentions have different drivers and therefore require different intervention strategies  Who producers report to (government vs. private vet) changes the drivers, and these behaviours need to be understood separately  Perceptions of responsibility are an important driver for both monitoring and reporting behavioural intentions

SC R

Abstract

In many countries, government strategies for biosecurity planning and outbreaks depend upon

U

private livestock producers being willing and able to conduct surveillance of their animals and the timely reporting of suspicious signs of disease. From a behavioural perspective, these

A

N

two kinds of behaviours – surveillance and reporting – should be treated separately when

M

developing a biosecurity plan in which producers play a key role in the prevention, detection, and reporting of animal diseases. Having an effective surveillance system in place is

ED

conceptually and practically independent of a reporting system that is both feasible and trustworthy. The behavioural intentions of 200 Australian producers to monitor their

PT

livestock and report to either the government or a private vet were measured in a structured telephone interview. Structural equation modelling revealed that these intentions had

CC E

different statistical relationships with a common set of predictor variables. Moreover, classification of the producers based on belief about monitoring and surveillance resulted in

A

three contrasting groups. These results are discussed in terms of their meaning for the development of behavioural strategies to promote surveillance and monitoring of animal disease. Keywords: behaviour, exotic disease, monitoring, reporting, surveillance.

2

1. Introduction Prompt reporting of signs of exotic diseases is fundamental to an effective government response and delays in detection can be very costly (Carpenter, O'Brien, Hagerman, & McCarl, 2011). The detection of an outbreak and effective and timely responses to it demand strong relationships between animal producers, agribusiness

IP T

institutions, regulatory agencies at various levels of government, and veterinarians (vets). In

Australia, as in many other countries, biosecurity outcomes are reliant on animal producers to

SC R

survey their stock for suspicious symptoms and report animal ill-health and deaths to

government bodies with legislative responsibility for biosecurity (Palmer, Fozdar, & Sully,

U

2009).

N

The responsibility of livestock surveillance falls predominantly to the individual

A

producer given their relative proximity to their animals, their personal interest in maintaining

M

the good health of their stock, and the cost effectiveness of the arrangement for government and the agricultural sector (Doherr & Audige, 2001). As a result, government strategies for

ED

biosecurity planning and outbreaks depend upon private livestock producers being willing and able to conduct surveillance of their animals and the timely reporting of suspicious signs

PT

of disease (Bronner, Hénaux, Fortané, Hendrikx, & Calavas, 2014). Producers must be able to devote resources to the task of livestock surveillance which might be a considerable task

CC E

given that stock, in some parts of the agricultural sector in Australia and some other countries, are free to range over a vast amount of space. Further, for diseases that have been

A

absent from a region for some time, or never observed in living memory, the ability of both producers and vets to accurately detect them is compromised (Elbers, GorgievskiDuijvesteijn, Van der Velden, Loeffen, & Zarafshani, 2010). Having an effective surveillance system in place is conceptually and practically independent of a reporting system that is both feasible and trustworthy. The Reasoned Action

3

Approach outlines that in addition to attitudes and social norms, individuals must perceive that the behaviour is within their abilities (Fishbein & Ajzen, 2010). Producers must believe that upon detecting an anomaly in the health of their stock, reporting the incident in good time to a vet or a government authority is both possible and the best thing to do. If high veterinarian fees make this more difficult to do (e.g., due to remoteness) then producers may

IP T

be more willing to rely on their own knowledge or take the time to consult with their

neighbours. If producers lack faith in the ability of private vets in their region to adequately

SC R

service the needs of the agricultural sector, or do not trust relevant government agencies to effectively deal with the issue in a manner that treats them fairly, then behavioural theory

U

suggests that producers’ intentions to report suspicious indicators of disease may not align with the desired reporting behaviour.

N

From a behavioural perspective, these two kinds of behaviours – surveillance and

M

A

reporting – should be treated separately when developing a biosecurity plan in which producers play a key role in the prevention, detection, and reporting of animal diseases. This

ED

is because the factors that contribute to the probability of surveillance and reporting behaviours being enacted by producers are likely to be different and, consequently, will

PT

require different interventions to encourage and maintain them over time and in different locations. Furthermore, it is likely that the causal accounts of the two kinds of behaviours

CC E

will vary with a host of factors including the type of livestock enterprise, scale, and the historical record of outbreaks in the region.

A

Determinants of Biosecurity Behaviours Research on the determinants of biosecurity behaviours has generally not considered both monitoring and reporting. For example, Palmer et al. (2009) studied three types of consultation: consultation with a government health advisor about worrying symptoms; consultation for a recent disease event; and, consultation for deaths. These behaviours can be

4

considered reporting behaviours which obviously implies some level of monitoring. Furthermore, other studies have focused on specific preventative measures that producers might undertake such as improved fencing and vaccination (e.g., Heffernan, Nielsen, Thomson, & Gunn, 2008; Garforth, Bailey, & Tranter, 2013) without explicit reference to either monitoring or reporting behaviours.

IP T

Surveillance behaviours need to be seen as important procedures in the day-to-day operation of agricultural enterprises which, in itself, assumes a level of responsibility for

SC R

detecting disease and preventing its spread (Higgins, Bryant, Hernández-Jover, McShane, & Rast, 2016). For animal producers, attributions of responsibility for biosecurity may fall to

U

one or more of a variety of stakeholders, including themselves, all producers, certain types of

N

producers, and to government and non-government institutions and organisations operating at

A

the state, federal and local levels (Nairn, Allen, Inglis, & Tanner, 1996; Ellis-Iversen et al.,

M

2010). Attributions of responsibility have been well-researched in the context of understanding cooperation and pro-social behaviour (Schwartz, 1977; Schwartz & Howard,

ED

1982). However, recent reviews (Mankad, 2016; Ritter et al., 2017) indicate that relatively little empirical research on monitoring and reporting exotic disease has considered producers’

PT

attributions of responsibility for these different activities despite notions of shared responsibility underpinning biosecurity strategies (Nairn et al., 1996). Heffernan et al. (2008)

CC E

underscored the importance of responsibility for collective action in concluding that “biosecurity measures were largely viewed by [their] study participants as an externally imposed

A

solution to an externally imposed problem” (p.369). Higgins et al. (2016) provide a nuanced analysis of stakeholder responsibility that is

intimately tied to three different kinds of “institutional logics” through which various stakeholders engage in biosecurity policy and practice. From their data analysis, the authors identified the “neoliberal logic” which is characterised by declining public resources for the

5

provision of biosecurity services with a concomitant attribution of responsibility to industry groups and farming organisations. In the “productivist logic” issues of commercial viability significantly influence responses to biosecurity, such that producers are unwilling to undertake biosecurity activities in the absence of commercial incentives or unless it aligns with a commercial imperative (Jones et al., 2015). Finally, the “agrarian logic” considers

IP T

exotic disease as a problem that emerges from beyond national boundaries such that responsibility for controlling these threats is attributed to national governments.

SC R

In addition to accepting responsibility for biosecurity behaviours and incorporating these behaviours into their routines, producers need to feel that they are capable of the required action, or this is likely to result in inaction (Bronner et al., 2014). Belief in one’s

N

U

capacity to perform a behaviour – what Bandura (1997) referred to as “self-efficacy” – has a

A

strong influence on attitudes towards both surveillance and reporting (Ellis-Iversen et al.,

M

2010), but it can also inadvertently breed over confidence leading producers to believe that they can delay reporting in order to manage outbreaks themselves (Vergne et al., 2014).

ED

Producers are often part of social networks that influence their behaviours via normative information about what behaviours are likely to be approved or disapproved of by

PT

one’s peers (i.e., “injunctive norms”) and how other people in the network are actually behaving (i.e., “descriptive norms”) (Fishbein & Ajzen, 2011). There can be stigma attached

CC E

to being the first farmer to report a disease because peer producers may attribute this as mismanagement of some kind (Vergne et al., 2014). Moreover, false alarms can be regarded

A

by some as being just as detrimental to the financial standing of agribusinesses as poor surveillance and reporting (Elbers et al., 2010). Social networks can also be important in the development of relationships of trust in rural communities (Sobels, Curtis, & Lockie, 2001). Factors that are important in encouraging engagement with health officials include the approachability and accessibility of

6

the government personnel, and their helpfulness and willingness to listen (Palmer et al., 2009; Bronner et al., 2014). A number of theorists have pointed to different kinds of trust (Vaske, Absher, & Bright, 2007). One distinction is between trust as honesty and integrity, on-theone-hand, and trust as competence and confidence on-the-other-hand. Earle, Siegrist, & Gutscher (2010) for example, argue that “social trust” is determined by the extent to which

IP T

one’s own salient values are perceived to align with those of another. “Confidence,” in contrast, is the belief that future events will unfold in a manner consistent with one’s

SC R

expectations based on the past performance of another. That is, producers may trust that another stakeholder will act consistently with their shared values, but the actions of that

U

stakeholder will be less than competent given an experiential record of poor past

N

performance.

A

The relationship between trust and perceptions of risk of an exotic disease outbreak has been examined in the biosecurity literature, which is unsurprising given that higher levels

M

of trust can operate as a kind of ‘antidote’ to high levels of perceived risk (Siegrist et al.,

ED

2005). But, while producers can perceive the impact of an outbreak as having national consequences, its perceived risk of occurring on their own farm is a relatively low probability

PT

event, and a low priority issue when compared with hazards such as bushfires and floods (Palmer et al., 2009).

CC E

Model Development

The model developed for this study takes its basis from behavioural theories,

A

predominantly the Reasoned Action Approach (Fishbein and Ajzen, 2010), the Trust, Confidence & Cooperation Model (Earle et al., 2010), and the Norm Activation Model (Schwartz, 1977). This theory was supplemented by the other contributing factors described above, namely trust, responsibility, risk perception and past/current behaviours.

7

The predictors of biosecurity behaviours discussed above can be organised into a simple, recursive model amenable to empirical tests. Figure 1 displays such a model by which background factors (i.e., factors that do not have direct effects on behaviour, such as, producer, property and business characteristics) are connected to biosecurity behaviours through individual beliefs and intentions. Background factors can shape beliefs but they can

IP T

also impact behaviour indirectly in model tests when all relevant belief mediators are

accounted for. This model can be subject to empirical tests using survey data and structural

SC R

equation modelling (Hayduck, 1996). More difficult, however, is obtaining valid and reliable measures of actual biosecurity behaviours. For this reason, in subsequent analyses, we focus

U

on employing the model to understand behavioural intentions, which are commonly theorised

N

to be the immediate antecedents to behaviour (Fishbein & Ajzen, 2010). In this way, the direct (and relevant indirect) effects of the background factors and beliefs on intentions to monitor and

A

report can be ascertained. However, it should be noted that behavioural intentions are not perfect

M

predictors of actual behaviours. The intention-behaviour gap has been noted in relation to the

ED

execution of multiple behaviours (Sheeran, 2002). Whilst behavioural observations are the gold standard, for practical reasons, observing livestock producers in Australia is not feasible due to the

PT

distance. Furthermore, while monitoring behaviours could be observed, reporting behaviours are infrequent and unpredictable in nature. Thus, this study uses behavioural intentions as a predictor of

CC E

behaviour which has demonstrated adequate predictive power of behaviour (Armitage & Conner, 2001).INSERT FIGURE 1

A

1.1 Study Objectives

Failure to appreciate that surveillance and reporting are different behaviours,

although necessarily related within a biosecurity response plan, can disarm efforts to mobilise and support producers to achieve policy objectives. It is important that biosecurity research is informed by current developments in social psychology in order to reduce the risk of over simplifying behavioural relationships and misunderstanding producers’ responses to policy 8

initiatives (Burton, 2004). In this spirit, this study has three main objectives. First, to test the comparability of monitoring and reporting behavioural intentions in terms of the range of determinants discussed earlier (i.e., attributions of responsibility, trust, competence, selfefficacy, norms, and risk perceptions). This type of approach seeks to understand how producers view surveillance and reporting behaviours and identifies conditions under which

IP T

behaviour is more or less likely to be adopted. The second objective is to classify producers

based on the behavioural intention determinants in order to identify segments that are more or

SC R

less amenable to biosecurity surveillance and reporting. The third objective is to propose

behaviour change strategies that might be implemented to support biosecurity surveillance

U

and reporting plans. This research approach recognises that the success of biosecurity plans is

N

contingent on the voluntary participation of landowners and, therefore, it endeavours to lay solid theoretical and empirical grounds for biosecurity strategies which have the effect of

2. Materials and Methods

ED

2.1 Participants

M

A

supporting animal producers’ decision-making and behaviour.

A stratified random sample of livestock producers was selected to represent approximately

PT

equal samples across the sheep, cattle and pig industries. Approximately equal samples of sheep, cattle, mixed sheep and cattle, and pig producers were invited, however, pig producers

CC E

were difficult to recruit as this is a more intensive industry and with less independent farms. Attempts were made to contact a total of 978 producers, however a response was not received

A

from all producers. Follow-up calls were made until 200 completed computer-assisted telephone interviews (CATI) were completed with a participate rate of 20.4%. Two-hundred and eleven producers refused participation citing lack of interest or time. No incentives were provided for participation. Interviews were only conducted with individuals who were responsible for farm management decisions. Sixty-seven percent of producers identified

9

themselves as the main decision-maker concerning how their livestock is monitored for disease while the remainder of the sample classified themselves as joint decision-makers. The sample of producers was predominantly male (90%), aged 55 years or more (70.5%), and reported gross annual on-farm income of less than AU$500,000 (64.5%). Participants reported owning beef cattle (n=53; 26.5%), sheep (n=50; 24.5%), mixed sheep

IP T

and cattle (n=52; 26.5%), pigs (n=13; 6.5%) and dairy cows (n=32; 16%). Average herd or

flock numbers at each property for each sector were: beef cattle (1,031), sheep (2,923), sheep

SC R

and beef cattle (2,952 and 355, respectively), pigs (2,550), and dairy cows (328). Dairy producers reported the smallest average distance to a preferred vet (27.4km) and pig

U

producers reported the largest (134.2km). Average property sizes ranged from 576ha (dairy)

N

to 10,858ha (sheep and cattle).

A

A minority of participants (n=24; 12.5%) had experience with managing an exotic

M

disease in the past. Just over half the participants who had experience with an exotic disease reported to their vet rather than to a government vet (n=14; 7%, see Table 1). Just over a third

ED

(n=9; 36%) stated that they reported the information to a government department and 8% (n=2) did not report at all. These latter two individuals stated that the government contacted

INSERT TABLE 1

CC E

PT

them.

2.2 Procedures

The variables measured in the CATI included those discussed in the previous section:

A

behavioural intentions, injunctive and descriptive norms, self-efficacy, perceived risk, social trust and institutional competency, and ascriptions of responsibility (refer to Table 2 for the question wording). These variables were identified on the basis of two focus groups and five

10

interviews conducted with producers to explore concepts arising from a literature review.1 These qualitative procedures identified key concepts which were then used to inform the development of a computer assisted telephone interview (CATI) designed to be administered over a 20-minute duration. A range of individual demographic, business and property characteristics were also measured in the CATI including the following: type of production

IP T

including beef cattle, sheep, cattle and sheep, dairy, and pigs; reported size of property (km2); reported distance from a preferred vet (km); past personal experience with managing an

SC R

exotic disease; age; gender; and annual gross business income. 2.3 Data Screening

IBM SPSS (Version 22) was used to screen the data for errors, non-normal distributions, and

N

U

missing values. There were small numbers of missing values and “don’t know” responses that

A

were replaced with estimates calculated using the multiple imputation method in LISREL9.2. The highest proportion of missing values (8%) occurred in response to the injunctive

M

norm item “Expecting you to monitor the livestock for exotic diseases is not a good

ED

biosecurity strategy because other producers don't do it enough.” Non-normality was addressed in subsequent analyses by employing maximum likelihood robust estimation

3. Results

PT

procedures in MPlus-6 (Muthén, 2010).

CC E

3.1 Measurement of Variables in the Behavioural Model The items used to measure the factors are shown in Table 2 along with their response options,

A

descriptive statistics and confirmatory factor loadings. Of note are the relatively low means and high standard deviations for the items measuring the injunctive norm suggesting participants did not regard normative influences as particularly strong, while personal

1

The participants were recruited from contacts provided by the Department of Agriculture and Water Resources and represented South Australian sheep and cattle producers (n = 17), Australian representatives of wool producers (n = 12) and pig producers (n = 5).

11

responsibility for monitoring livestock was relatively high on average. The confirmatory factor analysis showed good fit on a number of indicators (MLMV 2(df) = 364.41 (330), p = 0.09; RMSEA (95%CI) = 0.02 (0.00, 0.04); SRMR = 0.05; CFI = 0.95). The loadings were moderate to high in magnitude and statistically significant at p = .001. INSERT TABLE 2

IP T

The analysis supported the measurement of three behavioural intention variables. The first and third latent variables in Table 2 refer to intentions to report either to government or

SC R

to a private vet. The second latent variable refers to participants’ willingness to monitor

livestock. The latent intentions to report to a private vet and to monitor livestock, as well as

U

the perceived likelihood of an exotic disease outbreak, were measured with single indicators and their error variances were fixed to values that assume reliability equal to 0.60 (Hayduk,

N

1996). These reliabilities were chosen to be consistent with the loadings on the “intention to

M

3.2 Latent variable correlations

A

report to government” factor.

ED

The correlations between the latent variables are presented in Table 3 and invite two observations. First, the pattern of correlations between the intention variables and the other

PT

variables in the analysis are not the same. Intentions to monitor livestock (Variable 12 in

CC E

Table 3), had its strongest correlations with participants’ self-efficacy for recognising and managing emergency diseases (Variable 3) and their attributions of personal responsibility for monitoring livestock (Variable 9). In contrast, intentions to report to government or to a

A

private vet were not significantly associated with these variables. Finally, intention to report to a private vet (Variable 13 in Table 3) was significantly and positively correlated with the other intention factors. In sum, all three intention variables showed differences in their correlations with other variables. INSERT TABLE 3

12

The second observation from the data in Table 3 is that some of the potential predictors of intentions were significantly correlated with one another. For example, attributions of institutional responsibility for monitoring and managing emergency disease (Variables 7 and 8, respectively) were highly correlated ( = 0.76, p < .001) as might be expected. Further, judgements about participants’ local community’s competence in

IP T

preventing an outbreak (Variable 6) were significantly correlated with their attributions of descriptive norms (Variable 2), self-efficacy (Variable 3) and personal responsibility

SC R

(Variable 9) suggesting that some participants believed that local people involved in animal production were well-placed to meet the challenge of dealing with emergency diseases.

U

However, the correlations among the predictor variables pose a potential

N

multicollinearity problem. Thus, a smaller set of predictors was selected that were reasonably

A

independent of each other and significantly correlated to one or more intention factors.

M

Attributions of personal responsibility for monitoring livestock, attributions of institutional responsibility for monitoring and attributions to all landowners were deemed to be suitably

ED

independent set of predictors for further analysis. Background variables comprising demographic and property characteristics were also

PT

included in the structural equation model (i.e., past experience with a disease outbreak, age,

CC E

income, gender, distance from a preferred vet, property size, dummy variables for type of production with beef production serving as the base category). The correlations of these variables with the belief and intention variables were small and infrequent. Where production

A

type was concerned, the only significant relationships involved beef production and selfefficacy ( = 0.17, p < .05); pig production and local competence ( = 0.15, p < .05); intention to report to government with both sheep ( = 0.15, p < .05) and dairy production ( = -0.17, p < .05); and intention to monitor livestock with both beef production ( = 0.17, p < .05) and a mix of sheep and beef ( = 0.20, p < .01). Of the property characteristics, the only 13

significant correlations were between intentions to report to a vet and property size ( = 0.29, p < .000) and distance from a preferred vet ( = -0.28, p < .000). Of the demographic variables, participants’ age was correlated with injunctive norm ( = 0.15, p < .05), intention to report to government ( = 0.15, p < .05) and intention to report to a private vet ( = 0.19, p < .01).

IP T

3.3 Model Testing

Goodness of fit statistics indicate the model fits well: MLMV 2(df) = 94.93 (81), p = .14;

SC R

2/df = 1.17; RMSEA (95%CI) = 0.03 (0.00, 0.05); SRMR = 0.04; CFI = 0.97). The RMSEA, SRMR, CFI and 2/df are all below accepted levels of good fit (Tabachnick and Fidell, 2007).

U

Examination of the statistically significant parameter estimates in Figure 2 show that the

N

background variables had little effect in explaining the responsibility variables. Most of the

A

explanatory power of the model owed to the direct effects of the independent variables.

M

INSERT FIGURE 2 The structural equation model (SEM) was re-estimated following the removal of

ED

variables that failed to make a significant contribution to the explanation of the three

PT

intention factors (i.e., pig production, annual turnover, gender, and past experience). This resulted in a simplified model that included only the direct effects of exogenous variables.

CC E

The results of this model are shown in Table 4. The SEM showed good fit across all indices (MLMV 2(df) = 77.80 (70), p = 0.24; 2/df = 1.11; RMSEA (95%CI) = 0.02 (0.00, 0.05); SRMR = 0.04; CFI = 0.98). The results showed Greater willingness to monitor livestock was

A

accounted for by holding stronger feelings of personally responsible for monitoring livestock. Lesser willingness to monitor was associated with stocking both beef cattle and sheep and ascribing responsibility to government and other institutions for monitoring livestock. The linear combination of variables served to explain 45.5 percent of the variance in intentions to monitor livestock (p < .000). 14

Predictive variables for intentions to report to government had modest explanatory power (R2 = 0.111, p = .038). Positive intentions corresponded with smaller property size, stronger attributions of responsibility to government institutions for monitoring livestock and not being involved in dairy production (refer to Table 4 for statistical tests). One explanation of the negative effect of dairy production on intention to report to government is that dairy

IP T

producers, on average, are more negatively disposed toward government than producers in

other industries. However, the data indicated that dairy producers were not less trusting of the

SC R

Department of Agriculture and Water Resources (DAWR) on average (t(198) = 0.79, p > .05) than were other producers. But, the correlation between trust in the DAWR and intentions to

U

report to government was 0.59 (p < .01) in the dairy group compared with a correlation of

N

0.09 (p > .05) for the rest of the sample. The difference in magnitude between these two

A

correlations is statistically significant (z = 2.89, p < .01). However, this finding may owe to

M

the unequal sizes of producer subsamples, and future research may consider oversample among small producer groups and apply weights in the analysis.

ED

INSERT TABLE 4 Intentions to report to a private vet were more likely if participants believed that all

PT

landowners were responsible for monitoring livestock for exotic diseases, were older, involved in sheep production, and were located on smaller properties. Approximately 26

CC E

percent of the variance in intentions was accounted for by these variables (p = .008). 3.4 Classifying Producers

A

A cluster analysis resulted in the formation of three clusters of producers with group sizes equal to 96, 37 and 67 participants (i.e., Clusters 1 - Supportive, 2 – Monitor but not report, and 3 – Not my problem respectively). See Table 4 for statistical results. ANOVA (with Brown-Forsythe robust tests of equality of means) showed that cluster membership varied with scores on nearly all of the variables (see Table 5). Only injunctive social norm (F(2,

15

197) = 1.81, p > .05) and perceived risk (F(2, 197) = 0.40, p > .05) did not contribute significantly to the formation of the clusters. Results of post-hoc Bonferroni and Tamhane tests for all variables indicated that producers in Cluster 1 - Supportive (N = 96) had significantly higher means scores than both Clusters 2 – Monitor but not report and 3 – Not my problem for beliefs about trust in the DAWR, landowners’ responsibility for monitoring

IP T

and intention to report to a private vet (see Table 5). These results suggest that producers in Cluster 1 - Supportive might be supportive of undertaking surveillance activities and

SC R

reporting either to government or to a private vet. INSERT TABLE 5

U

Cluster 3 – Not my problem(N = 67), in contrast, tended to comprise producers who did not support undertaking surveillance and reporting activities to a private vet. This group

A

N

were more likely than producers in Cluster 2 – Monitor but not report to intend to report to government, but were inclined to see the task of monitoring to be primarily the responsibility

M

of government bodies. Individuals in Cluster 3 – Not my problem were least likely to

ED

acknowledge the existence of a descriptive norm regarding monitoring and reporting, had the lowest self-efficacy to recognize disease and manage an outbreak, were less likely to see

PT

local stakeholders as competent to manage disease, and felt the least personal responsibility for monitoring. Producers in Cluster 3 might be regarded as those for whom monitoring was

CC E

not seen to be their problem.

Cluster 2 – Monitor but not report, producers, the smallest of the three groups (N =

A

37), held the least amount of trust in the DAWR, were least supportive of government institutions having responsibility for monitoring activities and managing disease outbreaks, were less willing to attribute monitoring responsibilities to landowners, and less likely to want to report to government or to a private vet. Cluster 2 producers, however, did acknowledge personal responsibility for monitoring and intended to do so more than those

16

producers in Cluster 3 – Not my problem. These results suggest that Cluster 2 producers did not see a strong role for agricultural institutions such as government agricultural departments and were prepared to take responsibility for monitoring their own livestock, but were unwilling to report signs of exotic disease. 4. Discussion

IP T

This study identified a number of predictors to better inform biosecurity strategies and

practice. The results clearly demonstrate that the monitoring and reporting behavioural

SC R

intentions have different determinants suggesting different behaviour change strategies are

appropriate. These three behaviours should not be considered as interchangeable and assumed

U

that one intervention will result in improvement in all three.

N

4.1 Livestock Surveillance

A

The biggest influence on intentions to monitor livestock, was if producers believe it is their

M

responsibility to do so. Taking personal responsibility increases the likelihood that producers will embed surveillance activities into their day-to-day procedures. Therefore, reinforcing the

ED

key role that producers play in the larger picture of biosecurity preparedness, negotiating the delineation of roles and responsibility between producers and other institutions can promote

PT

the importance and benefits associated with taking charge of their own surveillance program. For example, producers benefit from taking responsibility for monitoring their livestock with

CC E

reduced livestock mortality and morbidity, lower veterinary expenses and diminished business interruption (Melyukhina, 2016).

A

Furthermore, producers who were less supportive of government being involved in

monitoring their livestock were more inclined to monitor themselves. This pattern of results also characterised Cluster 2 – Monitor but not report producers who minimised the responsibility of government and accepted some personal responsibility for the task of monitoring.

17

Finally, producers who stocked both beef and sheep are a group to target in engagement strategies aimed to promote livestock surveillance as they appear less inclined to monitor, presumably because of the difficulty in frequently observing stock who may roam extensive pastures. Other monitoring techniques, such as syndromic surveillance, may be appropriate in such circumstances (Brugere, Onuigbo, & Morgan, 2016). Syndromic

IP T

surveillance leverages collaboration with local producers and vets to look for changes in health information that may indicate a need for further investigation and can reduce the

SC R

burden on individual producers (Brugere et al., 2016). However, for syndromic surveillance to be effective, producers still need to be able to identify clinical signs of disease or

U

indications of ill-health and be able to report them.

N

Zero-order correlations demonstrated reasonably sized significant correlations

A

between intentions to monitor livestock and both descriptive norms and self-efficacy. That

M

is, believing that most other producers monitor their livestock and that one has the skills to undertake their own surveillance programme can promote monitoring intentions. Therefore,

ED

on-property demonstrations of monitoring strategies might be developed to help build capabilities among producers and also develop injunctive and descriptive norms (Minato,

PT

Curtis, & Allan, 2010). Key “local champions” who can demonstrate monitoring activities on their own properties, and help promote the importance of monitoring by showing that it is an

CC E

integral part of their own on-farm activities, can assist in up-skilling other producers and create or reinforce social norms.

A

4.2 Reporting to Government For reporting to government bodies, there are two key considerations. First, the correlation and SEM analyses showed that there is a tendency among some producers who are willing to report to government to believe that agricultural institutions (and not themselves or other landowners) should be responsible for both monitoring disease and managing it. Second,

18

producers with larger properties were less likely to report to government such that one might assume that an operation’s larger scale brings greater capacity and resources to deal with signs of disease in livestock without the participation of external parties. But, the data does not support this assumption. The correlations between property size and self-efficacy and perceptions of local competence were very small and not significant ( = 0.06 and  = .07,

IP T

respectively). Furthermore, producers on larger properties were less inclined to report

disease symptoms to either the government or to a private vet suggesting that reporting may

SC R

not be a priority behaviour in response to the potential existence of disease on larger scale properties. Rather, producers on larger properties may be inclined to euthanize livestock

U

showing suspicious symptoms or might believe that diseased livestock will die before an

N

infection can spread to other properties. These speculations, notwithstanding, additional data

A

is required to identify exactly what beliefs might mediate the effect of property characteristics

ED

4.3 Reporting to a Private Vet

M

such as size and remoteness on reporting intentions.

Intentions to report to a private vet were explained by property size, participants’ age,

PT

involvement in sheep production, and the belief that local producers and all landowners were

CC E

responsible for monitoring livestock. More positive intentions to report to a private vet were associated with older participants, smaller properties, cattle and pig production and if all landowners were thought to be responsible for monitoring. Note that, larger property size was

A

found to be associated with reluctance to report to either government or to private vets suggesting reporting itself, rather than who receives the report, is the issue for some large scale producers. Correlations showed that producers more remote from their preferred vets were less inclined to report to a vet, but this variable was not significant when included in the SEM

19

along with property size. It is plausible that the practicalities of reporting and engaging veterinary services are the biggest barrier for producers whose remote locations may make meeting with a veterinarian difficult. Palmer et al. (2009) found that the distance from the nearest vet was the only significant predictor of whether a producer consulted a vet. Another study found that when producers do contact vets, the main reason for subsequent visits not

IP T

occurring was due to either the problem being resolved over the phone or to the prohibitive

cost of the visit (East, Martin, & Anderson, 2013). Interventions that can make advice more

& Fernando, 2012; Kruger, Stenekes, Clarke & Carr, 2010).

U

4.4 Types of Producers

SC R

accessible for remote producers may increase reporting to either a vet or government (Cole,

N

Not all producers are alike with respect to their beliefs and intentions about monitoring and

A

reporting, but some are more alike than others. The cluster analysis formed three groups of

M

producers that had distinct profiles with respect to beliefs and intentions. The largest (48%) of these groups was supportive of undertaking surveillance activities and reporting. These

ED

producers might be regarded as having some beliefs that resemble the neoliberal logic identified by Higgins et al. (2014) given their apparent support for the devolving of

PT

biosecurity responsibilities to the producer. The co-management literature suggests that

CC E

interventions that provide producers with positive experiences when cooperating with government and other stakeholders on biosecurity issues can build trust over time (Robinson, Smyth, & Whitehead, 2005; Reed & Curzon, 2015). This literature highlights the concepts of

A

social learning, adaptive capacity and shared decision-making as crucial ingredients in the development of collaborative management. Another sizeable proportion of the sample (33.5%) held beliefs indicated they may think the causes and solutions to biosecurity threats exist at the national level and therefore beyond their control. This group not only showed less intention to monitor, but also believed 20

that they and other producers lacked the ability to recognise disease and manage it. Nonetheless, they were willing to report to government and appeared supportive of government efforts to conduct monitoring. Interventions that closely link government activities and support for on-farm monitoring may be effective in increasing producers’ own monitoring behaviour given their generally positive views of government institutions.

IP T

The smallest of the three groups (19%) appeared to be negatively disposed toward government institutions (i.e., DAWR) and were unsupportive of government institutions

SC R

having responsibility for monitoring activities and managing disease outbreaks. However,

they were willing to monitor and manage disease in their own livestock and felt competent to

U

do so. Behavioural strategies that support these producers to act independently of government

N

to achieve biosecurity outcomes may provide some opportunity for engagement through non-

A

government entities. For these producers, the best reporting avenue may be through private

M

vets. However, to the extent that these producers share some affinity with a productivist logic, they may be willing to work with government if the right commercial incentives are

ED

provided. Therefore, strategies that include producers in building a business case for biosecurity which is consistent with their commercial imperatives may facilitate behaviour

PT

change.

4.5 Limitations and Future Research

CC E

This study suggests that the type of production (e.g., dairy) can moderate the relationship between belief and behavioural variables, but our data does not enable the estimation and

A

comparison of behavioural models within production types. However, future research on producers’ surveillance and reporting behaviours might recruit representative samples of different types of producers that enable the statistical comparison of model parameters across samples. This multi-sample approach would support conclusions about the extent to which

21

production types moderate variable relationships of interest, but also whether the concepts in the models have the same meaning for different types of producers. Further research is also required to identify and understand the beliefs that mediate the effects of various property characteristics (e.g., property size, distance from a vet). Some possible mediators have been discussed in this research (e.g., access to veterinarian services,

IP T

barriers to surveillance on large properties) and elsewhere (e.g., Palmer et al., 2009); Higgins et al., 2016) Research that focuses on sheep and beef production may be best placed to

SC R

address these questions of mediation given that these sectors are less intensive than dairy and pig production.

U

5. Conclusions

N

There are clearly a number of factors that play into the complex biosecurity interface of

A

producers, industry, and government. This paper has helped to delineate some of this

M

complexity and separate behavioural intentions so that they can be more clearly understood and targeted to achieve better outcomes. Monitoring and reporting behaviours are not the

ED

same with respect to their determinants and this means that interventions aimed at behaviour change will require different approaches. Furthermore, reporting behaviours (i.e., to

PT

government or to a private vet) are also different types of behaviours such that a single behaviour change strategy is unlikely to be appropriate.

CC E

Finally, attributions of responsibility and responsibility denial are important concepts

for understanding producers’ cooperation with monitoring and reporting strategies. For some

A

producers, all landowners are regarded as being responsible for monitoring livestock and to investigate suspicious symptoms with a private vet. Personal responsibility for monitoring, on the other hand, is important in understanding why producers monitor their own livestock especially if government institutions are denied some or all of that responsibility. Other producers believe that government bodies are responsible for monitoring and signs of exotic

22

disease should be reported to them. Even if government is judged to be trustworthy, monitoring and reporting by producers does not necessarily follow directly. There is a lack of cohesion within the agriculture sector where biosecurity activities and their coordination are concerned. Developing and implementing effective behaviour change interventions will benefit from fostering participative stakeholder decision-making

IP T

procedures capable of building trust and fairness by addressing divergent attributions of responsibility for biosecurity activities.

SC R

Funding

This work was supported by the Department of Agriculture and Water Resources, Australian

U

Federal Government. The funding body played no role in the study design, data collection

N

and analysis or decision to publish.

A

CC E

PT

ED

M

The authors declare no conflicts of interest.

A

Conflict of Interest

23

References Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta‐ analytic review. British Journal of Social Psychology, 40(4), 471-499. Bandura, A. (1997). Self-efficacy: The exercise of control: Macmillan. Bronner, A., Hénaux, V., Fortané, N., Hendrikx, P., & Calavas, D. (2014). Why do farmers and

surveillance system in France? BMC Veterinary Research, 10(1), 93 - 105.

IP T

veterinarians not report all bovine abortions, as requested by the clinical brucellosis

SC R

Brugere, C., Onuigbo, D. M., & Morgan, K. L. (2016). People matter in animal disease surveillance: Challenges and opportunities for the aquaculture sector. Aquaculture, 476, 158 - 169. doi:http://dx.doi.org/10.1016/j.aquaculture.2016.04.012

U

Burton, R. J. (2004). Reconceptualising the ‘behavioural approach’in agricultural studies: a socio-

N

psychological perspective. Journal of Rural Studies, 20(3), 359-371.

A

Carpenter, T. E., O'Brien, J. M., Hagerman, A. D., & McCarl, B. A. (2011). Epidemic and economic impacts of delayed detection of foot-and-mouth disease: a case study of a simulated outbreak

M

in California. Journal of Veterinary Diagnostic Investigation, 23(1), 26-33.

ED

Cole, S., & Fernando, A. N. (2012). The value of advice: Evidence from mobile phone-based agricultural extension. Harvard Business School Working Paper, No. 13–047.

PT

Doherr, M., & Audige, L. (2001). Monitoring and surveillance for rare health-related events: a review from the veterinary perspective. Philosophical Transactions of the Royal Society of London

CC E

B: Biological Sciences, 356(1411), 1097-1106. Earle, T. C., Siegrist, M., & Gutscher, H. (2010). Trust, risk perception and the TCC model of cooperation. Trust in risk management: Uncertainty and scepticism in the public mind, 1-50.

A

East, I., Martin, P., & Anderson, M. (2013). A survey of large animal practice veterinarians: Their response to producer contact and familiarity with emergency animal diseases. Unpublished Work. Elbers, A., Gorgievski-Duijvesteijn, M., Van der Velden, P., Loeffen, W., & Zarafshani, K. (2010). A socio-psychological investigation into limitations and incentives concerning reporting a

24

clinically suspect situation aimed at improving early detection of classical swine fever outbreaks. Veterinary Microbiology, 142(1), 108-118. Ellis-Iversen, J., Cook, A. J., Watson, E., Nielen, M., Larkin, L., Wooldridge, M., & Hogeveen, H. (2010). Perceptions, circumstances and motivators that influence implementation of zoonotic control programs on cattle farms. Preventive Veterinary Medicine, 93(4), 276-285.

IP T

Fishbein, M., & Ajzen, I. (2010). Predicting and changing behavior: The reasoned action approach: Taylor & Francis.

Garforth, C., Bailey, A., & Tranter, R. (2013). Farmers’ attitudes to disease risk management in

SC R

England: a comparative analysis of sheep and pig farmers. Preventive Veterinary Medicine, 110(3), 456-466.

U

Hayduk, L. A. (1996). LISREL issues, debates and strategies: JHU Press.

N

Heffernan, C., Nielsen, L., Thomson, K., & Gunn, G. (2008). An exploration of the drivers to bio-

Veterinary Medicine, 87(3), 358-372.

A

security collective action among a sample of UK cattle and sheep farmers. Preventive

M

Higgins, V., Bryant, M., Hernández-Jover, M., McShane, C., & Rast, L. (2016). Harmonising

ED

devolved responsibility for biosecurity governance: The challenge of competing institutional logics. Environment and Planning A, 48(6), 1133-1151.

PT

Jones, P., Marier, E., Tranter, R., Wu, G., Watson, E., & Teale, C. (2015). Factors affecting dairy farmers’ attitudes towards antimicrobial medicine usage in cattle in England and Wales.

CC E

Preventive Veterinary Medicine, 121(1), 30-40. Kruger, H., Stenekes, N., Clarke, R., & Carr, A. (2010). Biosecurity engagement guidelines: practical advice for involving communities. Science for decision makers. Barton, ACT: Australian

A

Government Bureau of Rural Sciences.

Mankad, A. (2016). Psychological influences on biosecurity control and farmer decision-making. A review. Agronomy for Sustainable Development, 36(2), 1-14. Melyukhina, O. (2016). Producer incentives in livestock disease management: a synthesis of conceptual and empirical studies. Draft Report. Retrieved from OECD Conference Centre, Paris, France. 25

Minato, W., Curtis, A., & Allan, C. (2010). Social norms and natural resource management in a changing rural community. Journal of Environmental Policy & Planning, 12(4), 381-403. Muthén, L. (2010). Mplus User’s Guide, (Muthén & Muthén, Los Angeles). Mplus User's Guide,(Muthén & Muthén, Los Angeles). Nairn, M., Allen, P., Inglis, A., & Tanner, C. (1996). Australian Quarantine: a shared responsibility.

IP T

Canberra: Department of Primary Industries and Energy. Palmer, S., Fozdar, F., & Sully, M. (2009). The effect of trust on West Australian farmers' responses to infectious livestock diseases. Sociologia Ruralis, 49(4), 360-374.

SC R

Reed, M., & Curzon, R. (2015). Stakeholder mapping for the governance of biosecurity: a literature review. Journal of Integrative Environmental Sciences, 12(1), 15-38.

U

Ritter, C., Jansen, J., Roche, S., Kelton, D. F., Adams, C. L., Orsel, K., . . . Barkema, H. W. (2017).

N

Invited review: Determinants of farmers' adoption of management-based strategies for infectious disease prevention and control. Journal of Dairy Science, 100(5), 3329-3347.

A

Robinson, C. J., Smyth, D., & Whitehead, P. J. (2005). Bush tucker, bush pets, and bush threats:

ED

Biology, 19(5), 1385-1391.

M

cooperative management of feral animals in Australia's Kakadu National Park. Conservation

Rutkowski, A.-F., Van de Walle, B. A., van Groenendaal, W. J., & Pol, J. (2005). When stakeholders

PT

perceive threats and risks differently: the use of group support systems to develop a common understanding and a shared response. Journal of Homeland Security and Emergency

CC E

Management, 2(1).

Schwartz, S. H. (1977). Normative influences on altruism. Advances in Experimental Social Psychology, 10, 221-279.

A

Schwartz, S. H., & Howard, J. A. (1982). Helping and cooperation: A self-based motivational model. Cooperation and Helping Behavior: Theories and Research, 327-353.

.Sheeran, P. (2002). Intention—behavior relations: a conceptual and empirical review. European Review of Social Psychology, 12(1), 1-36. Siegrist, M., Gutscher, H., & Earle, T. C. (2005). Perception of risk: the influence of general trust, and general confidence. Journal of Risk Research, 8(2), 145-156. 26

Sobels, J., Curtis, A., & Lockie, S. (2001). The role of Landcare group networks in rural Australia: exploring the contribution of social capital. Journal of Rural Studies, 17(3), 265-276. Tabachnick, B. G. & Fidell, L. S. (2007). Using Multivariate Statistics (5th ed.). NY: Allyn and Bacon. Vergne, T., Guinat, C., Petkova, P., Gogin, A., Kolbasov, D., Blome, S., . . . Nathues, H. (2014).

IP T

Attitudes and beliefs of pig farmers and wild boar hunters towards reporting of African Swine fever in Bulgaria, Germany and the Western part of the Russian Federation. Transboundary

A

CC E

PT

ED

M

A

N

U

SC R

and Emerging Diseases, 63(2), e194 - 204.

27

Table 1: Reporting behaviour of participants who experienced exotic disease. Individual or Organisation Producers Reported To

N (%) 14 (56)

Neighbours

7 (28)

State Dept. Primary Industry

6 (24)

Federal Dept. Agriculture and Water Resources

3 (12)

IP T

Their vet

Industry association

2 (8) 2 (8)

SC R

Did not report Stock health management consultant

1 (4)

Table 2: Descriptive statistics and measurement properties of variables (N=200). Mean

SD

Injunctive Norm

Farmers in your district will think badly of you if you report an emergency disease † Expecting you to monitor the livestock for emergency diseases is not a good biosecurity strategy because other producers don't do it enough † Reporting agencies expect too much of you when it comes to monitoring emergency diseases in your livestock †

3.74

3.29

0.59

4.73

3.08

0.49

4.88

3.05

0.65

Most producers in your district would expect you to report symptoms of emergency diseases as soon as you see them in your livestock † Most producers in your district who you respect regularly monitor their livestock for symptoms of emergency diseases † Most producers in your district would report emergency diseases when they find them in their livestock †

8.83

1.47

0.47

6.99

2.50

0.54

7.56

1.96

0.62

You are confident in your ability to recognise an emergency disease in your livestock † If there was an emergency disease outbreak on your farm, you would be able to manage the threats on your own before they became too serious † How competent do you believe the following individuals and organisations are, in their potential to prevent an outbreak of an emergency disease?: Yourself ‡

7.21

2.58

0.68

5.60

3.12

0.45

6.72

2.42

0.88

Perceived Risk

The cross-product of: There is very little risk of an emergency livestock disease outbreak originating outside your property† An emergency disease outbreak would have a large impact on your farm business †

59.63

26.52

0.77

Trust in the Dept. Agriculture and Water Resources

To what extent do you agree that the Federal Department of Agriculture and Water Resources can be trusted to: Make good management decisions regarding emergency disease issues§ Respond effectively to emergency disease risks in your district §

6.00

2.15

0.82

6.63

1.96

0.77

U

Indicator Descriptions

A

CC E

Self-Efficacy

PT

ED

Descriptive Norm

M

A

N

Latent Variables

28

Std. Loading

5.80

2.32

0.80

How competent do you believe the following individuals and organisations are, in their potential to prevent an outbreak of an emergency disease? : Local vets ‡ Other farmers in the district ‡ Stock health management consultants‡

7.81 6.19 6.45

2.19 2.08 1.97

0.64 0.79 0.53

How responsible do you think the following organisations should be for monitoring emergency diseases among your livestock?: Department of Agriculture and Water Resources ¶ Customs and border security agencies ¶ Primary Industry Departments in State Governments ¶

7.08 7.38 7.31

2.71 2.96 2.66

0.90 0.74 0.90

Institutional Responsibility for Managing Exotic disease

How responsible do you think the following organisations should be for dealing with emergency diseases among your livestock?: Animal Health Australia/ Livestock Biosecurity Network. ¶ Department of Agriculture and Water Resources. ¶ Primary Industry Departments in State Governments. ¶

7.80 7.72 7.87

2.24 2.31 2.31

0.81 0.89 0.85

Personal Responsibility for Monitoring

How responsible do you think the following organisations should be for monitoring emergency diseases among your livestock?: Yourself ¶ How responsible do you think the following organisations should be for dealing with emergency diseases among your livestock?: Yourself ¶

9.06

1.39

0.69

8.87

1.67

0.57

7.52 6.85

2.68 2.77

0.87 0.84

7.43

3.07

0.82

7.46

3.10

0.70

7.47

3.05

0.77

Local Competence in Preventing an Outbreak

A

N

U

SC R

Institutional Responsibility for Monitoring

IP T

Deal with producers affected by emergency diseases in a fair and equitable manner §

How responsible do you think the following organisations should be for monitoring emergency diseases among your livestock?: Farmers in your district ¶ All landowners ¶

ED

M

Landowner Responsibility for Monitoring

How likely are you to do the following: Call government hotline within a day if you suspect there might be an emergency disease ¶¶ Report symptoms of an emergency disease to a government vet within a day of noticing them ¶¶

PT

Intention to Report to Government

How likely are you to do the following: Monitor for symptoms of emergency disease on your property over the next 3 months ¶¶

CC E

Intention to Monitor Livestock

How likely are you to do the following: 8.91 2.10 0.77 Call a private vet within a day if you notice symptoms of an emergency disease in your livestock ¶¶ † Response scale ranged from 0 (“strongly disagree”) to 10 (“strongly agree”). ‡ Response scale ranged from 0 (“very incompetent”) to 10 (“very competent”). § Response scale ranged from 0 (“cannot be trusted at all”) to 10 (“can be trusted completely”). ¶ Response scale ranged from 0 (“not at all responsible”) to 10 (“totally responsible”). ¶¶ Response scale ranged from 0 (“extremely unlikely”) to 10 (“extremely likely”). Note: The term ‘emergency disease’ was used in the surveys as this is the common terminology used in Australia.

A

Intention to Report to Private Vet

29

I N U SC R

Table 3: Latent variable correlations Variable

1.

2.

2. Descriptive Norms

-.18

1.00

3. Self-efficacy

.05

.41***

1.00

.19

.27

-.01

1.00

5. Trust in Dept. Agriculture and Water Resources

-.05

.27**

.13

.17

1.00

6. Local competence in preventing an outbreak

.19*

.63***

.73***

.17

.30***

1.00

7. Institutional responsibility for monitoring

-.05

.01

.05

.10

.50***

.13

1.00

8. Institutional responsibility for managing

-.24**

.08

-.09

.00

.44***

-.03

.76***

1.00

9. Personal responsibility for monitoring

-.14

.42***

.53***

-.17

.02

.44***

.08

.10

1.00

10. Landowner responsibility for monitoring

.08

-.02

.04

-.14

.14*

.24*

.39***

.29***

.33***

1.00

11. Intention to report to government

-.03

.11

.12

-.11

.21**

-.04

.26**

.26**

.05

.12

1.00

12. Intention to monitor livestock

-.07

.39**

.42***

-.06

-.05

.27**

-.20*

-.04

.56***

-.04

.25*

1.00

13. Intention to report to private vet

-.14

.16

.10

.10

.04

.14

.21*

.25**

.02

.27**

.51***

.43***

Injunctive Norms

A

1.

CC E

PT

ED

M

4. Perceived Risk

** p < .01

4.

*** p < .001

A

* p < .05

3.

30

5.

6.

7.

8.

9.

10.

11.

12.

Table 4: Results of the structural equation model. Variable†

Dairy

Intention to Report to

Intention to Report to a

Livestock

Government

Private Vet

b

Beta

Sig.

b

Beta

Sig.

b

Beta

Sig.

-1.081

-.203

.012

-

-

-

-

-

-

-

-

-

-1.798

-.227

.005

-

-

-

-.653

-.176

.028

Sheep -

-

-

-.049

-.170

.020

-.052

-.326

.011

Age

-

-

-

-

-

-

.314

.202

.004

-.281

-.294

.000

.228

.193

.009

-

-

-

1.618

.597

.000

-

-

-

-

-

-

2.999

.545

.000

7.485

.455

.000

for monitoring Personal responsibility for

-

-

-

-

-

.210

.282

.002

.889

.000

1.891

.741

.000

.111

.038

.259

.008

M

R2

A

Error

-

N

for monitoring

U

monitoring Landowner responsibility

SC R

Property size

Institutional responsibility

†

IP T

Beef and Sheep

Intention to Monitor

Demographic and property variables were scaled in the following way: sheep (no = 0; yes = 1), beef cattle and sheep (no =

0; yes = 1), dairy cows (no = 0; yes = 1); property size (km2); age (18-14 = 1; 25-34 = 2; 35-44 = 3; 45-54 = 4; 55-64=5;

A

CC E

PT

ED

more than 65 = 6).

31

Cluster

Comparison

Difference between

Standard

(F(df))

Number

Cluster Number

Cluster Means

Error

Descriptive Norm

1

3

1.41

.21

.000

(F(2, 112.71) = 19.45, p < .000)

2

3

.92

.32

.014

Self-Efficacy

1

3

2.58

.34

.000

(F(2, 197) = 45.74, p < .000)

2

3

2.35

.36

.000

Trust in the DAWR

1

2

1.99

.41

.000

(F(2, 93.79) = 15.14, p < .000)

1

3

.62

.25

.041

3

2

1.37

.41

.004

Local Competence

1

3

1.99

.23

.000

(F(2, 103.90) = 32.45, p < .000)

2

3

1.26

.35

.002

Institutional Responsibility for Monitoring

1

2

4.29

.42

.000

(F(2, 108.12) = 58.12, p < .000)

3

2

3.65

.46

.000

Institutional Responsibility for Managing

1

2

3.62

.41

.000

(F(2, 73.36) = 56.89, p < .000)

3

3.29

.41

.000

Personal Responsibility for Monitoring

1

3

1.23

.21

.000

(F(2, 129.34) = 22.01, p < .000)

2

3

1.09

.26

.000

1

2

3.41

.50

.000

1

3

1.95

.33

.000

3

2

1.46

.55

.028

Intention to Report to Government

1

2

2.13

.51

.000

(F(2, 100.64) = 7.57, p < .001)

3

2

1.57

.54

.012

Intention to Monitor Livestock

1

3

3.24

.44

.000

(F(2, 110.09) = 24.46, p < .000)

2

3

2.46

.65

.001

Intention to Report to a Private Vet

1

2

2.36

.62

.002

(F(2, 48.50) = 11.23, p < .000)

1

3

0.92

.21

.000

A

CC E

PT

(F(2, 95.60) = 30.55, p < .000)

SC R U

N A

M

Landowner Responsibility for Monitoring

2

Notes: Cluster 1 – Supportive, Cluster 2 – Monitor but not report, and Cluster 3 – Not my problem

respectively).

32

Sig.

IP T

Dependent Variable

ED

Table 5: Results of Post-hoc Tests of Differences between Cluster Means

IP T SC R U N A

A

CC E

PT

ED

M

Figure 1: A belief-mediated model of biosecurity intentions and behaviours

33

IP T SC R U N A M

Figure 2: Statistically significant model parameter estimates

A

CC E

PT

ED

Note: one-headed arrows indicate prediction, two-headed arrows indicate correlations between latent variables.

34