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USING BEST-WORST SCALING TO EXPLORE PERCEPTIONS OF 1

RELATIVE RESPONSIBILITY FOR ENSURING FOOD SAFETY 2

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PUBLISHED  IN  FOOD  POLICY  4

Seda  Erdem,  Dan  Rigby,  and  Ada  Wossink.  (2012).  “Using  Best-­‐Worst  Scaling  to  5 Explore  Perceptions  of  Relative  Responsibility  for  Ensuring  Food  Safety“.  Food  6 Policy,  37(6):  661-­‐670.  7

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Abstract 9

We examine stakeholders’ perceptions of the share of the overall responsibility of each stage in 10

the food supply chain has in ensuring that the meat people cook and eat at home is safe to 11

consume. We elicit these perceptions of relative responsibility via surveys using the Best-Worst 12

Scaling technique and analyse the data via Bayesian estimation of mixed logit models. Results 13

are reported for two groups of stakeholders: consumers and farmers, and for two meat food 14

chains: chicken and beef. The results reveal that consumers tend to think farmers are more 15

responsible for ensuring meat safety than farmers do. Similarly, farmers tend to think 16

consumers have a greater degree of responsibility than consumers believe they have 17

themselves. Such beliefs might affect stakeholders’ willingness to take actions and reduce 18

hazards in the supply chain. From a policy perspective, the research findings provide useful 19

insights to support policymakers and other decision-makers in the industry in developing 20

mitigation strategies. Communication with consumers and farmers about emerging food safety 21

problems in a supply chain and their involvement in proactive practices would need to be 22

attuned to their subjective perceptions of relative responsibilities in order for integrated risk 23

management systems to be effective. 24

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JEL codes: Q12, Q18, Q51. 26

Keywords: Best Worst Scaling; Maximum Difference Scaling; responsibility; perceptions; 27 food safety. 28

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2

Introduction 30

There has been a growing concern over the human health risks posed by biological, chemical, 31

and physical hazards in the food chain since the first appearance of the term ‘food scares’ in the 32

print media in the mid-1980s (Knowles et al., 2007). An increase in the number of reported 33

food poisoning cases and media coverage have emphasised the need for better safety practices 34

and management of control and information systems (Henson and Caswell, 1999; Garcia et al.; 35

2007; Mensah and Julien, 2011). 36

Following the BSE crisis in 1996, food safety administration underwent reorganization at the 37

EU level and this contributed to an overhaul of existing frameworks for the governance of food 38

safety in most member states (e.g., Bergeaud-Blackler and Ferretti, 2006; Halkier and Holm, 39

2006; Lenz, 2006; Nielsen, 2006). The outcome has been a shift in the focus from a food 40

provisioning perspective to consumer interests and the protection of consumer health. In 41

addition, a change in the societal perspective of the role of the consumer led to modifications of 42

the governance of food risks in many countries as well as at the EU level (Holm and Halkier, 43

2009; Houghton et al., 2008). In the new regulations individual consumers are called upon as 44

independent actors who need to be fully informed about food products and need to be engaged 45

in the decision making process. More integrated management systems that take into account the 46

linked nature of the stages of the food chain (i.e., feed supplier to fork) have been adopted 47

which recognize that at each stage of the food chain, including the domestic kitchen, has a 48

potential impact on the risks to humans associated with the consumption of unsafe foods. 49

Consumers are increasingly assigned responsibility for self-protection (Fisher et al., 2005) 50

rather than the historically more passive role as solely food purchasers. 51

Recent research has focussed on how food risks and food safety are perceived by consumers 52

(e.g., Williams and Hammitt, 2001; Magnusson and Hurti, 2002; Redmond and Griffith, 2004; 53

Siegrist et al., 2009; Webster et al., 2010; Marette et al., 2010) and how they are traded-off 54

against other food characteristics by consumers (e.g., Loureiro and Umberger, 2011; van 55

Wezemael et al., 2011). Another line of research has investigated consumers’ trust in various 56

agents that provide information about food safety, new food technologies and products, as well 57

as consumers’ responses to communication about food risk management (e.g., Frewer et al., 58

2003; van Kleef et al., 2006; Lenz, 2006; Mazzochi et al. 2008; Cope et al., 2010). In contrast, 59

there is little research on how consumers perceive their own responsibility for ensuring food 60

safety and locate this within a broader perspective on responsibility along the chain as a whole. 61

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The willingness of consumers and other food chain participants to take actions that reduce 62

hazards to human health is influenced by many factors. These include their awareness of these 63

hazards, their ability to take action and the extent to which they think their actions will reduce 64

hazards, as well as the extent to which they believe that even if they do undertake risk-reducing 65

behaviours, the effects of these are, or will be, reduced or eliminated by the actions of others in 66

the chain. In addition, there is variation among individuals’ sense of personal responsibility and 67

willingness to fulfil their own responsibility. 68

The limited literature on consumers’ perceptions of food safety responsibility has a distinct EU 69

perspective. The findings are divergent despite the narrow geographical focus. Kjaernes et al. 70

(2007) surveyed respondents from six EU countries to investigate perceptions of responsibility 71

with respect to food safety, nutrition, animal welfare and the promotion of a healthy diet. 72

Through qualitative statements, consumers compared their own responsibility for food safety 73

with that of governmental agencies. The study did not include other stages of the food chain. 74

The results showed considerable variations between countries with Italians most likely to ‘fully 75

agree’ that consumers were more responsible than government for food safety (35%), followed 76

by Britons (34%), Portuguese (30%), Danes (27%), Germans (25%), and Norwegians (13%). 77

Similarly, Krystallis et al. (2007) presented European consumers and experts with statements 78

on responsibility for protection from food risks, along with other issues, such as consumers’ 79

awareness of food safety, priorities in food risk management, and the role of the media. 80

Krystallis et al. found that consumers emphasised their own responsibility whereas experts 81

viewed food safety as a shared responsibility of mainly the state and producers with a limited 82

role for self-protection by consumers. A contrasting result is reported by van Wezemeal et al. 83

(2010) who conducted focus group sessions in Germany, Spain, France, and the UK and asked 84

beef consumers to indicate who they think is responsible for beef safety. They found that 85

consumers perceived their own responsibility for beef safety as low and tended to allocate 86

responsibility to actors in the early stages of the supply chain, such as farmers, abattoirs, and 87

inspectors. Leikas et al. (2009) found that Finnish consumers allocated responsibility 88

differently depending on the type of food risk, with higher perceived personal controllability 89

predicting higher personal responsibility. This study also showed that the more the food risk is 90

viewed as man-made rather than naturally occurring, the more responsibility is assigned to 91

industry, retailers and society as opposed to consumers themselves. 92

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In this paper, we analyse both consumers’ and farmers’ perceptions of the relative 93

responsibility associated with sequential stages of the meat supply chain. Compared to the 94

related works discussed above, this research contributes to the literature in three ways. First, we 95

contrast the perception of two stakeholder groups (consumers and farmers) of the responsibility 96

for ensuring food safety in the entire supply chain rather than at a specific stage. The supply 97

chain in our study is composed of ten stages. The multi-dimensionality of the research provides 98

us with more information on the perceptions of food-safety responsibility across the food chain 99

as well as on the difference between stakeholder groups. The approach could, and we believe 100

should, be extended to include agents at other stages of the chain, including abattoir workers, 101

retailers etc. We limit ourselves to just two groups to both highlight the approach and because 102

of the greater challenges involved in gaining access to those involved at some other stages. 103

Second, we focus on two different food systems, namely the beef and the chicken supply chain, 104

and thus compare different food risks. Recent food borne outbreaks and crises associated with 105

beef in the UK (e.g., E. coli 0157 outbreaks and BSE) and the scale of the disease burden from 106

Salmonella and Campylobacter associated with the handling of chicken prepared in domestic 107

kitchen, played an important role in the selection of these food types. 108

From a policy perspective, we believe that the investigation of perceptions of different 109

stakeholder groups on responsibility for food safety, when done at an early stage of strategy 110

development, can provide useful insights to support policymakers and other decision-makers in 111

the industry. For modification of the roles and responsibilities of actors in food safety systems 112

to be effective, account need to be taken of how stakeholders see their own role and 113

responsibility in the food chain. For example, stakeholders might think that their actions to 114

prevent hazards are (likely to be) undermined by others in the chain who feel less responsible 115

for the safety of the food considered. Such a dilemma may then result in a lack of motivation 116

for risk reducing actions. Policy-makers, in this respect, may control such situations by 117

identifying critical control points at each stage of the supply chain and communicating them 118

with each stakeholder group in the chain. This may also enhance awareness among 119

stakeholders about food safety and preventive controls at different stages of the chain. 120

The final contribution of the research is the means by which we investigate stakeholders’ 121

perceptions of relative responsibility. While the majority of papers exploring perceptions of 122

relative responsibility have used focus groups or, for example, responses to stimuli using Likert 123

scales (e.g., ‘strongly agree’ to ‘strongly disagree’) we employ a different methodology to 124

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identify rankings and scaled allocations of responsibility within the food chain. The innovative 125

technique employed is Best Worst Scaling (BWS), also known as Maximum Difference 126

Scaling, which allows direct assessment of perceptions of relative responsibility for food safety. 127

As we explain in Section 2 while BWS has been developed in the market research literature to 128

understand the relative importance of product or service attributes, we apply it to an abstract 129

concept namely ‘responsibility for food safety’, rather than to a product or a service as is 130

common in recent applications (Lusk and Briggeman, 2009; Mueller et al., 2009; Hein et al., 131

2008). 132

The rest of the paper is organised as follows: Section 2 introduces the methodology, the design 133

of the survey used in achieving the study’s objectives, and the data; Section 3 explains the 134

models estimated to analyse perceptions of responsibility; Section 4 discusses the results before 135

the paper is concluded. 136

1 Methodology 137

2.1 Best-Worst Scaling (BWS) Technique 138

Best-Worst Scaling (BWS) is a form of conjoint analysis that has recently attracted attention in 139

the food and health economics literature (Flynn et al., 2007; Lusk and Briggeman, 2009). The 140

aim is to elicit the relative weight or importance that respondents allocate to items in 141

(sometimes large) sets. Typically these items are product or service attributes where the 142

researcher seeks to understand their relative importance. BWS studies elicit this information in 143

a distinctive form which we now describe. The statistical model estimated with the BW data is 144

described in Section 3. 145

The BWS method was developed by Finn and Louviere (1992) as an extension of Thurstone’s 146

(1927) Method of Paired Comparison (MPC) to elicit consumer preferences. The main idea of 147

MPC is to elicit trade-offs between paired items. BWS is a more general version of MPC since 148

BWS allows the comparison of more than two items in a task in which a respondent chooses 149

the ‘best’ and ‘worst’ (or ‘most’ and ‘least’) items. The pair of items chosen as best/worst 150

exhibits the maximum difference in preference; hence the method is also called Maximum 151

Difference Scaling. The paired best/worst choice provides a great deal of information on 152

consumers’ preferences over the items featured in the set. To illustrate this, assume a subset of 153

four items (e.g., A, B, C, D) selected from a larger set. Assume a respondent chooses A as the 154

‘best’ and D as the ‘worst’ items among the four items. These two selections provide 155

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information on five of the six possible paired comparisons; we know the respondent prefers A 156

over B, C and D, and also prefers B and C over D. The quantity of preference information 157

obtained from the best/worst selection is the main advantage of BWS over MPC. This even 158

becomes more significant when respondents are asked to make a series of choices which is 159

typical in BWS surveys. 160

BWS is typically employed when preferences are sought over a large number of items. Their 161

ranking according to their importance/preferability may especially become difficult, and prone 162

to anomalous behaviour, when the number of items increases. Rather than ranking the full 163

(large) set of item, BWS breaks this task into more cognitively manageable size. As in the 164

example (i.e., A, B, C, and D), a choice task comprises of a subset of a large set and 165

respondents are asked to make best and worst choices in a series of sets. The amount of 166

information obtained from such repetitive tasks is too large. 167

Other advantages of BWS concern task comprehension and the avoidance of a scale-use bias. 168

The BWS approach is argued to be relatively easy for most respondents to understand. There is 169

evidence that people use better judgment when they only need to evaluate preferences at the 170

extreme rather than preferences in many levels (Louviere, 1993; Marley and Louviere, 2005). 171

As respondents only choose at the extreme, the process is considered ‘scale-free’ (Cohen and 172

Orme, 2004) eliminating the possibility of a scale-use bias (Baumgartner and Steenkamp, 173

2001). Scale bias may involve respondents using only a part of the scale. It avoids ambiguity 174

and differences in interpretation which are endemic to approaches which record responses to 175

stimuli on Likert or Visual Analogue scales. For example, Likert-based approaches require 176

respondents to map their preferences on to the scale in the same way, that is, interpret terms 177

such as ‘strongly agree’ in the same way. Such ambiguity is absent when one only has to select 178

the most and least important items within a set. 179

The BWS approach has been used recently in a number of research areas to evaluate public 180

perceptions of intangible attributes. For example, Auger et al. (2007) examined the attitudes of 181

consumers towards social and ethical issues, such as recycling and human rights, across six 182

countries while Flynn et al. (2007, 2008) examined elderly people’s evaluation of quality of life 183

in terms of different attributes such as attachment, security, enjoyment and control. 184

In this paper, we use BWS to characterise the share of overall responsibility for ensuring food 185

safety that farmers and consumers assign to each stage in the food chain. The collected best-186

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worst data is analysed to retrieve the estimates for the relative size of responsibility of stages 187

for respondents, which is then used to measure the rankings of all stages in the supply chain 188

with respect to their perceived level of responsibility. We do this, separately, with respect to the 189

beef and chicken that people cook and eat at home. 190

2.2 Survey Design 191

The meat supply chain referred to in the surveys comprised ten stages: feed supplier, farmer, 192

live animal transport, abattoir, meat transport, processor, wholesaler, retailer, and consumers. 193

We further classified retailers as either ‘supermarket’ or ‘local butcher’ to analyse whether 194

respondents view the responsibility associated with these different types of retailer differently. 195

This insight is of interest in particular given food poisoning outbreaks associated with butchers 196

in the UK These include the 2005 E. coli outbreak in South Wales in which contaminated meat 197

was sold by a butcher who did not comply with several Food Safety Regulations resulting in 198

157 identified cases (Pennington, 2009) and the 1996 E. coli outbreak in Scotland in which a 199

butcher sold unfit meat to the public resulting in 496 cases of E. coli O157 (The Pennington 200

Group, 1998). We did not include competent authorities (FSA, Defra, etc) among the supply 201

chain stages to which responsibility shares were to be assigned. This would have introduced 202

ambiguity and overlap into the process – for example if one viewed abattoirs as the most 203

responsible stage within a set, and thought they should undergo a more rigorous inspection 204

regime, then it would be unclear as whether the abattoir or these competent authorities should 205

be identified as the most responsible stage within the set. 206

The experimental design comprised eight blocks of eight sets, and each set included five items 207

(i.e., stages). Each block was then randomly assigned to each respondent. As mentioned, the 208

number of items included in the BWS tasks is important. Tasks comprising more than five 209

items have been to shown to result in greater confusion and fatigue undermining the extra 210

preference information the larger set can in principle yield (Cohen and Orme, 2004; Sawtooth 211

Software, 2007). 212

The selections of five stages in choice tasks satisfy the optimal design characteristics: 213

frequency balance, orthogonality, positional balance, and connectivity among tasks. One-way 214

frequencies show that each stage appears across all eight versions 32 times (i.e., perfectly 215

balanced). The two-way frequencies show that each stage appears 14 times on average with 216

every other stage with a standard deviation of 0.46 (i.e., nearly orthogonal). Positional 217

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frequencies show that each item appears 6.4 times on average with a standard deviation of 0.49. 218

After ensuring a balanced and nearly orthogonal survey design, tasks were randomised and a 219

participant was randomly assigned to a block. 220

Figure 1 shows an example BWS task used in the surveys. The final framing of the BW survey 221

questions was refined after piloting. This allowed us to identify confusions in wording, lack of 222

understanding, and the time needed to complete the survey. After this pilot, we fine-tuned our 223

design and revised some of the descriptions and presentations rather than the substantive core 224

of the questionnaires. Two versions of the survey were used: the first asked people where they 225

thought lay the greatest responsibility and the least responsibility for ensuring the beef they eat 226

is safe; the second asked the same question regarding chicken (hereafter referred to as ‘beef’ 227

and ‘chicken’ survey). 228

The reason for investigating perceptions of responsibility at beef and chicken supply chains is 229

that the prevalence of food cases is attributable to these products. As reported in Gormley et al. 230

(2011), most of the foodborne outbreaks recorded in England and Wales from 1992 to 2008 are 231

attributable to poultry meat (19.1%), followed by miscellaneous foods1(17.6%) and red meat 232

(15.7%). Focussing on two supply chains allows us to explore whether people perceive 233

responsibility differently in these chains due to recent food borne outbreaks and crises 234

associated with beef in the UK (e.g., E. coli 0157 outbreaks and BSE) and the scale of the 235

disease burden from Salmonella and Campylobacter associated with the handling of chicken 236

prepared in domestic kitchen. 237

2.3 Data Collection 238

We conducted the paper-pen surveys with two stakeholder groups: public and farmers in 239

England and Wales in the summer of 2009. We sampled the non-farming public (hereafter 240

called ‘consumers’) in the city of Manchester at various points randomly, and farmers at five 241

agricultural shows that have high coverage in the UK (Cheshire, Royal Welsh, Nantwich, 242

Garstang, and Romiley Young Farmers Shows)2. Among these shows, the Cheshire and Royal 243

1 Miscellaneous foods include buffet foods, sandwiches and other dishes comprising multiple ingredients.

2 We are aware that there is a possible selection bias in sampling only at agricultural shows. A desire to be able to speak to respondents and clarify any issues regarding interpretation of the survey, combined with the budget constraint, meant we opted to sample farmers at agricultural shows. A more

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Welsh Shows are the biggest agricultural summer events in the UK. There were approximately 244

80,000 and 220,000 visitors to the Cheshire and Royal Welsh Shows respectively, with farmers 245

from across the country attending the shows. Thus, the farming sample included farmers 246

experienced with a wide range of livestock production systems, such as beef, dairy, sheep, goat, 247

pig and poultry. More than half of the farming sample (51%) was made up of livestock farmers 248

(e.g., dairy, beef, goat, and sheep), 42% was both livestock and poultry farmers, and only 1% 249

was poultry farmers. We excluded from the study consumers who attended the farm shows by 250

asking them whether they work on farms, are involved in farming practices or help friends and 251

family. 252

We interviewed 267 respondents (110 consumers, 157 farmers) who were randomly assigned to 253

a beef or chicken survey approximately equally. Summary statistics for the respondents are 254

given in Table 1. The two samples are quite similar with respect to the variables, such as 255

gender, age, and having children or not. Both samples are female dominated and most 256

respondents have a completed degree as their highest qualification (28% consumers, 23% 257

farmers) and have full-time jobs (34% consumers, 36% farmers). A higher percentage of 258

consumers are in post-graduate education compared to vocational education, whereas a higher 259

percentage of farmers are in vocational education than in post-graduate education. A 260

comparison with 2001 UK census data shows that the respondents in our study were similar to 261

the general UK population with respect to age, gender, and employment status (majority 30-44 262

years old (23%), female (51%), and full-time employed (41%) in the population). 263

2 Model 264

We analyse respondents’ perceptions of the most and least responsible stages of the supply 265

chain and estimate their allocations of relative responsibility among the stages via estimation of 266

choice models based on Random Utility Theory (RUT). RUT, initiated by Thurstone (1927) 267

and generalised by McFadden (1974), is concerned with modelling human decision-making. It 268

does so in a probabilistic rather than deterministic framework. The general form of the RUT 269

model can be written as: 270

diversely distributed sample could have been achieved via postal or web based sampling but at the cost of personal interaction with respondents which was possible at the shows.

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ijtijtijt VU ε+= (1)

where ijtU is individual i’s utility3 from alternative j in choice set t={1,2,...T}, ijtV is the 271

deterministic part of the model which can be written as: 272

ijtiijt XV β=   (2)

where iβ is individual i’s utility parameter vector, ijtX is a vector for stages associated with 273

attribute j, and ijtε is the stochastic (random) component, which allows researchers to make 274

probabilistic statements about consumers’ behaviour (Adamowicz et al., 1998; Lusk, 2003). 275

In a BWS study, the respondent will choose a pair of attributes that maximises the utility 276

difference in the best/worst pair chosen. For example, assume the respondent chooses attribute j 277

and attribute k as the best and worst, respectively, out of a choice set with J items. Then the 278

probability that respondent i chooses attribute j as best and attribute k as worst is the probability 279

that the difference in utility between ijtU and iktU is greater than all M other possible utility 280

differences in the choice set, where M= J(J-1)-1. Assuming that ijtε is independently and 281

identically distributed (iid), then this probability can be written in a simple logit form: 282

( )( )∑ ∑ −−

−=

= =

J

l

J

mimtilt

iktijt

JVV

VVkj

1 1exp

expst)chosen wor is andbest chosen is Pr(

(3)

Here, the parameters belong to the utility difference function, not to the individual best or worst 283

utilities. As an individual chooses a sequence of alternative best-worst (or most-least) pairs for 284

each choice set t, we can write the probability of the sequence of choices as the product of the 285

logit form in equation (3) as: 286

3 For the sake of clarity, we set out the model in its typical form, where the respondent chooses the most preferred (non-preferred) items that maximise (minimise) utility. In this study we transpose that approach to analyse the chain stages seen as most and least responsible for ensuring food safety.

11

( )( )

∏

∑ ∑ −−

−==

=

= =

T

t J

l

J

mimtilt

iktijtii

JVV

VVL

1

1 1exp

exp)( choices) of (sequencePr β

(4)

Given that individuals are heterogeneous and have different tastes (or, in this case, different 287

assessments of relative responsibility) the logit form of the probability can be further specified 288

using Mixed Logit (MXL) formulation. The MXL, sometimes called random parameter logit, 289

model is widely used in the literature that acknowledges the preference heterogeneity of 290

consumers (Revelt and Train, 1998; Brownstone and Train, 1998; McFadden and Train, 2000). 291

It is a highly flexible model that can approximate any random utility model (Train, 2003; 292

McFadden and Train, 2000). 293

The mixed logit probability is then the weighted average of the logit formula evaluated at 294

different values of β, with the weights given by the density function of )( iβϕ . We assume that 295

β is distributed normally with mean b and covariance w. 296

The choice probability in equation (4) can then be written as: 297

iiiii dwbLP ββϕβ ),|()(∫= (5)

This is the probability of the individual’s sequence of choices conditional on the parameters of 298

the population distribution, ),|( wbiβφ . Generally, the integral in (5) does not have a closed 299

form (Brownstone and Train, 1998) therefore we approximate it through simulation. 300

We used Hierarchical Bayes (HB) simulation method for parameter estimation of the density 301

function. HB simulation follows an iterative procedure that uses each individual’s choices 302

along with information about the distribution of part worths for all respondents to estimate 303

individual-level parameters. 304

Each iteration consists of four steps (Sawtooth Software, 2003). The first step involves finding 305

a new estimate of b using the initial estimates for βs and w. We assume that b is distributed 306

normally with a mean equal to the average of βs and a covariance matrix equal to w divided by 307

the number of respondents. A new estimate of b is drawn randomly from that distribution. 308

Next, using the present estimates of βs and b, a new estimate of w is drawn from the inverse 309

Wishart distribution. This then follows by generating new estimates of the βs using the present 310

12

estimates of b, w, and σ. Here σ is the standard deviation of the random error term in equation 311

(1). And as a last step, a new estimate of σ is generated using the present estimate of b, w, and 312

βs. The final estimated parameters are then obtained by averaging the parameter values 313

retrieved from further iterations after an initial burn-in period of 10000 iterations. 314

3 Results 315

Stakeholders’ allocation of responsibility for food safety in the meat supply chain is derived 316

from their stated preferences. Thus, the results discussed in this section represent respondents’ 317

beliefs rather than actual measurements of the responsibility in the food chain. We present the 318

results in two parts. 319

First, we compare the assessments of relative responsibility of farmers (both livestock and 320

poultry producers) and consumers, for the beef and chicken supply chains in Sections 4.1 and 321

4.2 respectively. In doing so we formally test two hypotheses, namely that: (a) consumers and 322

farmers have the same allocations of relative responsibility for ensuring meat safety ‘across the 323

meat chain’, and (b) consumers and farmers have the same allocation of responsibility for the 324

safety of meat at ‘specific stages of the meat chain’. 325

Having compared the perceptions of these two groups of stakeholders, from opposite ends of 326

the food chain, we then compare responsibility perceptions across the beef and chicken chains 327

in Section 4.3. Given that majority of foodborne cases (i.e., 40%) are attributable to chicken 328

and red meat (Gormley et al., 2011) yet the transmission practices and pathways are often so 329

different between them, it is essential to examine differences in perceptions of stakeholders for 330

food safety responsibility in beef and chicken supply chains. 331

4.1. Stakeholders’ Perceptions of Responsibility in the Beef Supply Chain 332

Table 2 reports results from estimation of the mixed logit model in (5) for the beef supply 333

chain. For each stage of the chain, estimates of the mean and standard deviation of the 334

distribution of the responsibility scores are reported. Heterogeneity in individuals’ perception of 335

the degree of responsibility is represented via the standard deviation, which captures variation 336

or dispersion in individuals’ perceptions of responsibility from the mean estimate. The larger 337

the standard deviation (or spread), the more variability in perceptions of the sample. Standard 338

errors for estimates of both the mean and standard deviation are also reported. 339

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Our results show variation in individuals’ perceptions. Almost all estimates of the mean of 340

responsibility scores, for both the consumer and farmer, are statistically significant at 1% level. 341

The estimates of the standard deviations of the distributions of responsibility scores, for both 342

the consumer and farmer samples, are significantly different from zero in all cases, indicating 343

heterogeneity in the views regarding levels of relative responsibility. 344

The non-farming sample’s choices indicate that on average Processors are most responsible for 345

ensuring the food safety in beef supply chain, whilst the Live Animal Transport stage is seen as 346

having the lowest share of responsibility. To ease interpretation of the estimation results, we 347

convert the mean estimates to ratio-scaled scores that sum to 100 (hereafter called ‘shares’4). 348

For example, the share of responsibility allocated by farmers to Consumers is 14.7%, 349

approximately three times higher than that allocated to farmers themselves (4%). We compare 350

these shares of responsibilities placed on each stage with a level of share in which each stage is 351

‘naively’ assumed to have an equal degree of responsibility (i.e., 100%/10)5. 352

Assuming an equal share of responsibility among the ten stages (i.e., 10% each), we see that 353

both consumers and farmers believe that Processors and Supermarkets have a share of 354

responsibility statistically significantly higher than this average (10%) level, whereas the 355

Animal Transport stage is regarded as having a lower than average level of responsibility. 356

We investigate the significance of these similarities and differences in views by investigating 357

two hypotheses. The first hypothesis tests whether consumers and farmers have the same 358

pattern of allocations of responsibility for the safety of beef across the stages of the chain as a 359

whole. The second hypothesis tests whether they have the same perceptions of relative 360

responsibility for specific stages of the chain. 361

4 Shares of responsibility, which indicate for ten supply chain stages, the probability that individual i chooses stage j as the most responsible stage, are calculated using Multinomial Logit formulation

(Sawtooth, 2007): 1-a)exp(

)exp(

+β

β

ij

ij, where β ijare zero-centered logit scores, and a is the number of

items shown per set (in this case it is 5).

5 The 10% average level of responsibility was not conveyed to respondents in the survey; they were not asked to indicate whether a stage had above, or below, this level, nor did it feature in estimation. It simply allows ex-post comparison of the perceived responsibility of a stage with the mean responsibility level, which is simply an arithmetic mean from dividing 100% of responsibility across N items within the study.

14

Hypothesis A: Consumers and farmers have the same allocations of responsibility for the 362

safety of beef ‘across the supply chain’. 363

To test for systematic differences in the allocations of relative responsibility across stages and 364

between the two stakeholders, individual-specific logit scores were regressed on the stages and 365

the interaction of the stage and the group (i.e., type of stakeholder) considered: 366

𝛽!= α + γn stagen + δn (stagen*group) + εi with n = (1, …, 9) 6 (6)

where stagen with n= (1,…,9) is a binary variable distinguishing the nine stages; group is a 367

binary variable distinguishing the two stakeholders (1=farmer, 0=consumer), and α, γ and δ are 368

parameters to be estimated. 369

The null hypothesis of no significant differences between consumers’ and farmers’ allocations 370

of responsibility for food safety is H(A)0: ∑δn = 0 (overall distributions of individual-specific 371

scores for the two stakeholders are the same). The test result of the regression analysis in Table 372

3 shows that stakeholders’ perceptions of food safety responsibility in the beef chain are not 373

different from each other at 1% significance level (P>|t|= 0.03). 374

Although consumers and farmers have similar perceptions of responsibility for ensuring that 375

the beef in the supply chain is safe, there may still be differences in their views at specific 376

stages of the chain. Thus, we investigate this further in the following hypothesis: 377

Hypothesis B: Consumers and farmers have the same allocations of responsibility for the 378

safety of beef at a ‘specific stage of the chain’. 379

The null hypothesis here is H(B)0: δn = 0 for n = (1,..., 9) (the distribution of individual-specific 380

scores for stagen is the same for both stakeholders). Although the test result of Hypothesis A 381

reveals that consumers and farmers tend to have similar perceptions of food safety 382

responsibility, the results of tests of Hypothesis B shows that they do have different perceptions 383

of food safety at some stages. As seen from the test results in Table 3, consumers and farmers 384

perceive the responsibility of Feed Supplier, Farmer, Processor, and Consumer differently in 385

the beef supply chain. 386

6 We dropped one of the dummies for stages to prevent the dummy variable trap.

15

Notwithstanding the possible influence of different error variance between the two subsets of 387

respondents, our results show that the share of responsibility allocated by consumers to the 388

Farmer and Feed Supplier is higher than that allocated by farmers by a factor of 3.3 and 2.4, 389

respectively (see Table 2). The share of responsibility allocated by farmers to Processors and 390

Consumers is higher than that of consumers (approximately 1.2 and 2.5 times, respectively). 391

These differences in views are statistically significant at the 1% level, as seen from the test 392

results in Table 3. 393

The results also show that farmers tend to allocate less than 15% of total responsibility to the 394

first three stages of the chain (i.e., feed supplier 4.2%, farmer 4.4.%, live animal transport 395

3.6%), and almost 15% responsibility to Consumers. This observation of a less than an average 396

share of responsibility to one’s own stage of the chain is apparent also in the Consumer sample 397

where 6% of the responsibility is allocated to themselves but around 65% responsibility to 398

initial and mid-stages of the chain. Such beliefs may be due to recent E.coli outbreaks at an 399

abattoir (e.g., 2005 Welsh E.coli outbreak) and farm (e.g., 2009 E.coli outbreak at petting 400

farms) that received a lot of media attention or it may be just a case of individuals’ lack of 401

motivation to take responsibility. 402

4.2. Stakeholders’ Perceptions of Responsibility in the Chicken Supply Chain 403

Stakeholders’ perceptions of responsibility in the chicken supply chain show some similarities 404

to the results for beef supply chain. As seen in Table 4, almost all coefficient estimates in both 405

samples are statistically significant at 1% level, and significant standard deviations reveal 406

heterogeneity in preferences in both the samples. For consumers, on average, Processors and 407

Farmers are regarded as having the highest share of responsibility in the chicken supply chain. 408

For farmers, Processors and Supermarkets are seen as the most responsible stages. Both 409

samples view Live Animal Transport as the least responsible stage. Using eqn. (6), we repeat 410

the tests undertaken for beef as to whether consumers and farmers have the same perceptions of 411

responsibility ‘across the supply chain’ overall (Hypothesis C) and at ‘specific stages of the 412

chain’ (Hypothesis D). 413

Hypothesis C: Consumers and farmers have the same allocations of responsibility for the 414

safety of chicken ‘across the supply chain’: H(C)0: ∑δn = 0. 415

According to the test results in Table 5, responsibility perceptions of consumers and farmers for 416

the chicken supply chain as a whole show no statistically significant differences at 1% level 417

16

(P>|t|=0.67). Although both groups of stakeholders have similar perceptions of responsibility 418

across the entire supply chain they may allocate different levels of responsibility to specific 419

stages of the chain. We investigate this next. 420

Hypothesis D: Consumers and farmers have the same allocations of responsibility for the 421

safety of chicken at a ‘specific stage of the chain’: H(D)0: δn = 0. 422

Tests of Hypothesis D reveal that consumers and farmers view differently the degree of 423

responsibility associated with Feed Suppliers, Farmers, Butchers, Supermarkets, and 424

Wholesalers (see Table 5). 425

Processor and Supermarket are the stages that consumers and farmers allocate the largest share 426

of responsibility respectively. While consumers allocate slightly higher (approximately 11%) 427

responsibility score to Processor than farmers do, farmers allocate almost 70% more share of 428

responsibility to Supermarket than consumers do. Additionally, Farmers allocate approximately 429

4 times more responsibility to Processor than to Farmer (themselves), whereas consumers 430

allocate approximately 2.5 times more responsibility score to Processor than to Consumer 431

(themselves). The share of responsibility allocated by consumers to the Farmers and Feed 432

Supplier is at least as twice as high than that of farmers (see Table 4). We find that these 433

differences in views are statistically significant at the 1% level. 434

An interesting result from this investigation is that neither consumers nor farmers allocate 435

Consumers a level of responsibility statistically significantly different to the 10%, ‘equal’ share, 436

level, whereas in both chains consumers allocate a far greater responsibility share to Farmers 437

than farmers do themselves. Similar to the beef case, we find that farmers tend to allocate less 438

than an average 10% level of responsibility to themselves in the chicken supply chain. 439

Additionally, Consumers tend to allocate the highest shares of responsibility to the middle 440

stages of the meat food chain whilst farmers tend to allocate the lowest shares of responsibility 441

to first three stages of the chain (approximately 12%) and the highest shares of responsibility to 442

the mid (approximately 33%) and later stages of the chain towards consumers (approximately 443

55%). 444

17

4.3. Comparison of Stakeholders’ Perceptions of Responsibility in Beef and Chicken 445

Supply Chains 446

The results presented in Tables 2 and 4 reveal that the Processor is believed to be the most 447

responsible stage in both supply chains. For consumers, this is followed by Farmer, 448

Supermarket, and Abattoir; whereas for farmers it is followed by Supermarket and 449

Wholesaler. What is striking, considering the farmer’s perceptions of responsibility for food 450

safety in the beef and chicken supply chains, is the very low responsibility assigned by 451

farmers to the first three stages of the chain, including themselves. Consumers’ perception of 452

the responsibility of the early stages is very different. This contrast is stable across the two 453

meat supply chains considered. 454

Tests of the form used above are utilised to see whether the allocations of responsibility overall 455

are the same for the beef and chicken supply chains for the consumer and farmer samples. The 456

results from these tests are given below: 457

Hypothesis E: Consumers (or Farmers) have the same allocations of responsibility for the 458

safety of beef and chicken ‘across the supply chain’: H(E)0: ∑δn = 0 459

The test results show no statistically significant differences in relative responsibility for food 460

safety in the beef and chicken supply chains at the 1% level for consumers (P>|t|=0.61) (Table 461

6) and also for farmers (P>|t|=0.23) (Table 7). However, there are differences regarding the 462

responsibility at specific stages. 463

Hypothesis F: Consumers (or Farmers) have the same allocations of responsibility for the 464

safety of beef and chicken at a ‘specific stage of the chain’: H(F)0: δn = 0 465

The test results in Table 6 indicate that consumers perceive the food safety responsibility of 466

Processors and Supermarkets differently in beef and chicken supply chains. They believe that 467

Processors are more responsible in the chicken chain than in the beef chain and that 468

Supermarkets are more responsible in the beef chain than in the chicken chain. It is surprising 469

that consumers feel that Feed Suppliers, Farmers and Consumers do not have different level of 470

responsibilities between beef and chicken supply chains given the recent history regarding 471

scares and outbreaks associated with the two meats in the UK (e.g., E. coli and BSE in beef, 472

Salmonella and Campylobacter in chicken at domestic kitchens). Hence one may expect 473

different allocations of responsibility to these stages in beef and chicken supply chains. 474

18

The test results for farmers in Table 7 indicate that they place statistically significant share of 475

responsibility on Supermarkets and Consumers. The latter result indicates that farmers view 476

Consumers as more responsible in the beef chain than in the chicken chain. This is somewhat 477

surprising as cases attributable to chicken generally occur in domestic kitchens (Gormley et al., 478

2011), whereas recent well-known food-borne outbreaks attributable to beef (e.g., E. coli) have 479

been due to the mishandling of beef at the food services’ level, e.g., butcher, restaurants 480

(Gormley, 2011; Pennington, 2009). Thus, it would have been conceivable to observe a higher 481

perceived responsibility for consumers regarding chicken than beef. 482

The test result further implies that farmers think that other stages, such as the Feed Supplier, 483

Farmer (themselves), and Abattoirs, are not systematically different with respect to the 484

responsibilities for ensuring safety in beef and chicken chains. For example, farmers consider 485

their own responsibility similar for the chicken and beef supply chains. They also believe that 486

abattoirs are approximately equally responsible for the safety of beef and chicken products. 487

Another result from the analysis also reveals that farmers tend to allocate approximately the 488

same share of responsibility (approximately 12%) to the initial three stages in both beef and 489

chicken supply chains. 490

4 Concluding Remarks 491

This paper investigates stakeholders’ opinions of the degrees of responsibility for food safety 492

for sequential stages in the food chain. The application is for meat prepared and consumed at 493

home and included ten food chain stages (from feed supplier to consumer). Opinions are 494

compared for two stakeholder groups (consumers and farmers) and two types of meat (beef and 495

chicken). 496

Survey data are collected employing the Best-Worst Scaling (BWS) technique. For subsets of 497

food chain stages, respondents are asked to identify the two stages they believe to be the ‘most’ 498

and the ‘least’ responsible for ensuring the meat people consume does not cause them to be ill. 499

The data are statistically analysed with mixed logit models estimated via Bayesian techniques. 500

This study is the first to employ BWS to investigate perceptions of responsibility and food 501

safety. BWS is typically employed when preferences are sought over a large number of items. 502

Feedback from respondents indicate that the BWS-tasks are easy to carry out as respondents 503

face only subsets of the full set of items (albeit repeated ones). Another important advantage of 504

19

the BWS technique is that the statistical analysis of the best-worst choice data provides us 505

direct estimates of the shares of the responsibility attributed to the food chain stages. 506

The results show that consumers tend to think farmers are more responsible for ensuring meat 507

safety than farmers do, whereas farmers tend to think consumers have a greater degree of 508

responsibility than consumers themselves believe. There is a consistent pattern of Farmers and 509

Consumers regarding their own stage of the chain as having a less than average (i.e., less than 510

10%) share of the overall responsibility for ensuring food safety. Consumers allocate 511

responsibility predominantly to the middle stages of the meat food chain. Conversely, farmers 512

allocate more responsibility to the later stages of the chain. We find this pattern both for the 513

chicken and beef chain. 514

Insights from our study may have implications for the development and implementation of food 515

risk management systems. In work on stakeholder involvement, the role of perceptions of 516

responsibilities has received little attention and, when addressed, it has mainly been to consider 517

socio-cultural differences across countries (Kjaernes et al,, 2007; Krystallis et al., 2007; 518

Wezemeal et al.,2010). The results in this paper provide a useful first glance at an important 519

behavioural aspect that needs to be accounted for in the food safety domain at the national 520

level. Communication with consumers and farmers about emerging food safety problems in a 521

supply chain and their involvement in proactive practices would need to be attuned to their 522

subjective perceptions of relative responsibilities in order for integrated risk management 523

systems to be effective. 524

According to the Food and Agriculture Organisation of the United Nations one way of 525

strengthening national food control systems is via addressing food issues from farm to table as 526

a continuum and “recognising that food control is a widely shared responsibility that requires 527

positive interactions between all stakeholders (FAO, 2003, p.10).” As seen from the findings of 528

this study, each stage of the supply chain holds a shared responsibility. However, the level of 529

responsibility these stages believe they have over ensuring food safety is observed to be 530

different. Particularly, for farmers and consumers, we observe the allocation of less than an 531

average share of responsibility to their own role in the meat supply chain. Some of the reasons 532

for such behaviour may be the lack of awareness regarding risks and how to handle them, and 533

the lack of motivation regarding willingness to comply with safe practices. For example, in one 534

of the largest E. coli outbreaks in Wales in 2005 there was evidence that the butcher sold unfit 535

cooked meat and was found to be the sole responsible party. However, the investigation also 536

20

found that other parties in the supply chain (e.g., schools, local council) did not comply with the 537

safe food practices. The roles and responsibilities of organisations and key people were not 538

clear and agreed between stakeholders, and the control and monitoring of working practices 539

and procedures were found to be insufficient (Pennington, 2009, p.13-15). Such a tragic event 540

could have been avoided if all terms and roles were clear and agreed by stakeholders involved. 541

This has provided a good lesson to stakeholders such that the Food Standard Agency issued 542

new guidelines in the wake of the inquiry. These included providing guidance on the use of 543

separate machinery for raw meat and ready to eat foods (Food Standard Agency, 2011). As a 544

policy-maker or a decision-maker it is thus necessary to understand how familiar stakeholders 545

are to food risks and their preventive methods, as well as to understand the extent to which 546

people think their (and others’) actions will reduce risks, before implementing a mitigation 547

strategy. 548

As an extension of the current research, we plan to survey a third stakeholder group, abattoirs, 549

representing a mid-chain group. Such an extension would give a further insight into how 550

stakeholders (initial, mid- and final chain links) allocate the responsibility and perceive their 551

own role in the chain. Another interesting group to interview would be food safety experts —552

the share allocation retrieved from these responses could be used to replace the naïve 553

assumption of equal shares of responsibility among the ten stages (i.e., 10% each). In addition, 554

in further research, it would be interesting to investigate consumers’ and farmers’ responses in 555

relation to their psychometric and socio-demographics characteristics (e.g., level of knowledge, 556

gender, etc) to shed light on how they make decisions. It would also be interesting to research 557

perceptions of the relative responsibility for more specific food safety issues with different 558

impacts on human health, in contrast to the generic description of food safety used here which 559

encompasses diverse aspects from short term food poisoning to longer term issues such as BSE. 560

Last, but not least, following-up stakeholders over a number of years may produce interesting 561

information on how perceptions change over time. This information would also provide insight 562

into the effectiveness of awareness campaigns. 563

Acknowledgement: 564

The research was supported by the Rural Economy and Land Use (RELU) Programme 565

(http://www.abdn.ac.uk/reluecoliproject/index.htm) via funding from the project ‘Reducing E. 566

coli O157 Risk in Rural Communities’ (RES-229-31-0003) and the University of Manchester, 567

UK. We thank members of the project team and seminar participants at the 2010 WCERE 568

21

Congress and the 2010 AAEA and AES meetings for valuable comments. We are also grateful 569

to anonymous reviewers for their valuable comments and suggestions that improved the quality 570

of the paper. 571

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705 706 707 708 709

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Tables and Figures 710

Table 1. Characteristics of Survey Respondents 711

Variables Consumers Farmers

Mean Std. Dev Mean Std.

Dev Gender (1=female, 0=male) 0.69 0.47 0.60 0.49 Age ( in years) 35.81 15.55 38.83 14.82 Income (annual household income in £) 43,274 26,405 38,611 29,379 Children (1=yes, 0=no) 0.39 0.49 0.43 0.50 Education (1=yes, 0=no)

No GCSE level education 0.00 0.00 0.08 0.27 GCSE Level education (e.g. GCSE, O-Levels or Standards)

0.11 0.32 0.18 0.38

A-Level education (e.g. A, AS, S-Levels, Highers) 0.21 0.41 0.18 0.38 Undergraduate education (e.g. University examinations but not completed degree)

0.19 0.39 0.09 0.29

Degree or Graduate education (e.g. BSc, BA) 0.28 0.45 0.23 0.42 Post-graduate education (e.g. PhD, MSc, MA) 0.14 0.35 0.07 0.26 Vocational education (e.g. NVQ, HNC, HND) 0.07 0.25 0.17 0.37

Occupation (1=yes, 0=no) Student 0.32 0.47 0.11 0.32 Full time employed 0.34 0.48 0.36 0.48 Part time employed 0.20 0.40 0.11 0.31 Unemployed (looking for a job) 0.00 0.10 0.01 0.11 Unemployed (not looking for a job) 0.00 0.00 0.01 0.11 Self-employed 0.05 0.23 0.32 0.47 Retired 0.05 0.23 0.04 0.20

Livestock farmers 0.51 0.50 Poultry farmers 0.01 0.11 Farmers having both livestock and poultry production

0.42 0.49

Number of respondents 110 157 712

713

714

715

716

25

Table 2. Relative Responsibility in the Beef Supply Chain: Mixed Logit Results 717

Consumer sample Farmer sample

Supply Chain Stages Meana Std.

Dev.a

Share of Responsibilityd

(%) Meana Std.

Dev.a

Share of Responsibilityd

(%) Feed supplier 1.35 1.81 10.0 -3.28 1.98 4.2

(0.64) (0.06) [7.2-12.9] (0.53) (0.07) [2.9-2.6]

Farmer 2.25 1.80 14.5 -4.28 2.14 4.4

(0.61) (0.06) [11.5-17.6] (0.62) (0.08) [2.6-6.2]

Live animal transport -0.04 1.66 3.4 -3.98 2.01 3.6

(0.51) (0.05) [2.3-4.4] (0.56) (0.07) [2.3-4.8]

Abattoir 2.31 1.67 12.6 -1.16 1.85 11.4

(0.48) (0.09) [10.7-14.6] (0.47) (0.06) [9.5-13.3]

Meat transport 1.08 1.62 5.9 -2.02 1.69 6.4

(0.44) (0.05) [4.6-7.2] (0.40) (0.05) [5.2-7.5]

Processor 2.69 1.60 14.7 0.24 1.63 18.3

(0.39) (0.04) [12.2-17.1] (0.37) (0.04) [16.7-19.8]

Retailer-supermarket 2.26 1.66 13.1 -0.51 1.59 13.7

(0.41) (0.04) [10.3-15.9] (0.30) (0.03) [11.8-15.6]

Retailer-local butcher 1.82 1.60 10.5 -0.99 1.51 11.6

(0.36) (0.04) [8.2-12.7] (0.28) (0.03) [9.9-12.8]

Wholesaler 1.90 1.60 9.3 -0.82 1.59 11.7

(0.41) (0.04) [8.2-10.4] (0.35) (0.04) [10.7-12.8]

Consumerc 0.00 0.00 6.0 0.00 0.00 14.8

[3.7-8.4]

[12.5-17.0]

Number of respondents 53 84 Root Likelihoodb 0.45 0.47 Notes: Numbers in parenthesis are standard errors; numbers in brackets are the 95% confidence intervals. a ‘Mean’ and ‘Std Dev’ denote the estimates of the mean and standard deviation of the distribution of the responsibility scores. b Root Likelihood measures the goodness of fit. As we have five alternatives in each choice set, the null probability of each alternative to be chosen is 0.20 (i.e., 1/5). This is the expected RLH value for a chance model. The actual values of 0.45-0.50 would be interpreted as just better than approximately 2.5 times the chance level. (Sawtooth Software Technical Paper, 2008). c Parameter for ‘consumers’ is normalised to zero for identification purposes. The estimated effects of the other issues are interpreted as the level of responsibility relative to the ‘consumers’. d This is the ‘average’ share of responsibility, calculated using the mean of distributions. 718

719

720

721

722

26

Table 3. Differences in Stakeholders’ Perceptions of Responsibility in the Beef Supply 723 Chain 724

H(A)0: ∑δn = 0 Coefficient (δn) Std. Error t-value P>|t| -8.78 4.14 -2.12 0.03**

H(B)0: δn = 0

Stage Coefficient (δn) Std. Error t-value P>|t|

Feed Supplier -5.73 1.38 -4.16 0.00***

Farmer -10.03 1.38 -7.27 0.00*** Processor 3.57 1.38 2.59 0.01*** Consumer 8.78 1.38 6.37 0.00***

Statistically significant at ***p<0.01, ** p<0.05, * p<0.10 725

726

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729

730

731

732

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734

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737

738

27

Table 4. Relative Responsibility in the Chicken Supply Chain: Mixed Logit Results 739

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Consumer sample Farmer sample

Supply Chain Stages Meana Std.

Dev.a

Share of Responsibilityd

(%) Meana Std.

Dev.a

Share of Responsibilityd

(%) Feed supplier 0.50 1.84 8.8 -1.09 2.16 4.5

(0.63) (0.06) [6.4-11.2] (0.67) (0.07) [2.8-6.1]

Farmer 1.17 1.82 12.1 -1.61 2.20 4.7

(0.69) (0.08) [9.2-14.9] (0.69) (0.08) [3.1-6.4]

Live animal transport -0.94 1.68 3.1 -1.29 1.98 2.2

(0.48) (0.05) [1.9-4.3] (0.56) (0.06) [1.5-2.9]

Abattoir 1.35 1.80 11.6 1.11 2.01 10.7

(0.54) (0.05) [8.9-14.2] (0.55) (0.06) [8.9-12.5]

Meat transport 0.71 1.71 7.8 0.69 1.82 7.9

(0.49) (0.05) [6.0-9.6] (0.47) (0.06) [6.8-9.0]

Processor 2.71 1.81 18.6 2.22 1.95 16.6

(0.59) (0.06) [16.5-20.8] (0.53) (0.07) [14.9-18.6]

Retailer-supermarket 0.65 1.68 10.0 2.41 2.02 17.0

(0.47) (0.04) [7.3-12.7] (0.55) (0.07) [15.0-19.0]

Retailer-local butcher 1.21 1.62 11.1 1.64 1.92 13.5

(0.43) (0.04) [8.7-13.5] (0.51) (0.06) [11.7-15.3]

Wholesaler 0.91 1.78 8.4 1.46 1.99 12.4

(0.57) (0.06) [6.7-10.1] (0.56) (0.07) [10.9-13.9]

Consumerc 0.00 0.00 8.5 0.00 0.00 10.4

[5.7-11.3]

[7.7-13.1]

Number of respondents 57 74 Root Likelihoodb 0.48 0.50 Notes: Numbers in parenthesis are standard errors; numbers in brackets are the 95% confidence intervals. a ‘Mean’ and ‘Std Dev’ denote the estimates of the mean and standard deviation of the distribution of the responsibility scores. b Root Likelihood measures the goodness of fit. As we have five alternatives in each choice set, the null probability of each alternative to be chosen is 0.20 (i.e., 1/5). This is the expected RLH value for a chance model. The actual values of 0.45-0.50 would be interpreted as just better than approximately 2.5 times the chance level. (Sawtooth Software Technical Paper, 2008). c Parameter for ‘consumers’ is normalised to zero for identification purposes. The estimated effects of the other issues are interpreted as the level of responsibility relative to the ‘consumers’. d This is the ‘average’ share of responsibility, calculated using the mean of distributions. 741

742

743

744

28

Table 5. Differences in Stakeholders’ Perceptions of Responsibility in the Chicken 745 Supply Chain 746

H(C)0: ∑δn = 0 Coefficient (δn) Std. Error t-value P>|t| -1.89 4.36 -0.43 0.67

H(D)0: δn = 0

Stage Coefficient (δn) Std. Error t-value P>|t|

Feed Supplier -4.29 1.45 -2.95 0.00***

Farmer -7.37 1.45 -5.07 0.00*** Butcher 2.53 1.45 1.74 0.09*

Supermarket 6.92 1.45 4.76 0.00*** Wholesaler 3.91 1.45 2.69 0.01***

Statistically significant at ***p<0.01, ** p<0.05, * p<0.10

Table 6. Beef vs. Chicken Supply Chains: Differences in Consumers’ Perceptions of 747 Responsibility 748

H(E)0: ∑δn = 0 Coefficient (δn) Std. Error t-value P>|t| 2.50 4.95 0.50 0.61

H(F)0: δn = 0

Stage Coefficient (δn) Std. Error t-value P>|t|

Processor -3.90 1.67 -2.33 0.03**

Supermarket 3.00 1.67 1.80 0.07* Statistically significant at ***p<0.01, ** p<0.05, * p<0.10

Table 7. Beef vs. Chicken Supply Chains: Differences in Farmers’ Perceptions of 749 Responsibility. 750

H(E)0: ∑δn = 0 Coefficient (δn) Std. Error t-value P>|t| -4.39 3.64 -1.20 0.23

H(F)0: δn = 0

Stage Coefficient (δn) Std. Error t-value P>|t|

Supermarket -3.26 1.21 -2.68 0.01***

Consumer 4.39 1.21 3.61 0.00*** Statistically significant at ***p<0.01, ** p<0.05, * p<0.10

29

Figure 1. An Example of a BWS Question 751

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