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Benefits of traceability in food markets: Consumers' perception and actionCristina Mora a; Davide Menozzi a

a Department of Economics, University of Parma, Parma, Italy

Online Publication Date: 01 June 2008

To cite this Article Mora, Cristina and Menozzi, Davide(2008)'Benefits of traceability in food markets: Consumers' perception andaction',Food Economics - Acta Agriculturae Scandinavica, Section C,5:2,92 — 105

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ORIGINAL ARTICLE

Benefits of traceability in food markets: Consumers’ perceptionand action

CRISTINA MORA & DAVIDE MENOZZI

Department of Economics, University of Parma, Via Kennedy 6, Parma 43100, Italy

AbstractFood traceability is generally considered as a tool which provides consumers with targeted information and which can facilitatethe withdrawal and recall of food and feed products. The aim of this research is to examine Italian consumers’ perception oftraceability. Two different products, chicken and honey, were examined in order to evaluate the main drivers of attitude andbehaviour towards traceable food. The theory of planned behaviour was used as a theoretical framework to identify mainattitude�behavioural relations. Scores on factor analysed and multi-dimensional concepts were used to segment consumersinto clusters with different profiles of attitude, perception, trust and habit with regards to food traceability. The resultingsegments were investigated for differences in willingness to pay and intention to purchase traceable chicken and honey. Thefindings could help to design targeted public and private interventions.

Keywords: Chicken, cluster analysis, factor analysis, honey, theory of planned behaviour, traceability, willingness to pay.

Introduction

Since the adoption of the ‘‘White Paper on Food

Safety’’ (European Commission, 1999), the

European Union’s food safety policies have followed

a comprehensive, integrated approach throughout

the food chain ‘‘from farm to table’’. In particular,

Regulation (EC) n. 178/2002 contains general

provisions for traceability, applicable from 1 January

2005, covering all food and feed, as well as food

and feed businesses. Unless provisions for specific

traceability exist, the requirement for businesses is

limited to ensuring the so-called ‘‘one step back�one

step forward’’ approach, through the identification

of the immediate supplier and the immediate

subsequent recipient of the product (European

Commission, 2004).

Food traceability standards aim to reduce the risk

of food-borne disease, providing consumers with

targeted information and facilitating the withdrawal

of food and feed products. Consumers benefit from a

reduction in the risk of ill-health and/or loss of life

due to food-borne disease, as well as a reduction in

information asymmetry, as they are supplied with

quality information. The magnitude of these bene-

fits, as well as different methodologies to evaluate

them are widely debated by economists. There is a

general agreement that the theoretically correct

measure of the value to consumers of improvements

in food safety is the maximum amount they are

willing to pay for a specific improvement (prevent

the risk of food-borne disease), or the amount they

are willing to accept to forgo improvement (accept

loss). Willingness to pay (WTP) is widely quoted in

the literature and has been employed in cost�benefit

analysis of food regulations (Caswell, 1995; Henson,

1996, 1997).

This study aims to empirically identify segments of

consumers clustered with similar attitudinal�beha-

vioural characteristics and perception of traceability;

secondly, the different WTP and intention to pur-

chase traceable products across the segments is

analysed. The rationale for the study is that a food

standard like traceability, as introduced by EC Reg.

178/2002, is perceived in different ways by consu-

mers. In particular, as previous research has shown,

consumers tend to associate traceability with food

safety of risky products and product recall as well as

Correspondence: Cristina Mora, Department of Economics, University of Parma, Via Kennedy 6, 43100 Parma, Italy. Tel: �39 0521 032469.

Email: cristina.mora@unipr.it

Food Economics � Acta Agricult Scand C, 2008; 5: 92�105

ISSN 1650-7541 print/ISSN 1651-288X online # 2008 Taylor & Francis

DOI: 10.1080/16507540903034907

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the guarantee of product quality expressed as related

information (Gellynck & Verbeke, 2001; Dickinson

& Bailey, 2005; Hobbs et al., 2005; Verbeke & Ward,

2006; Loureiro & Umberger, 2007; van Rijswijk

et al., 2008). Given these remarkable differences in

perception, there is little point in addressing average

consumer opinion. Different segments, in fact, need

to be treated in different ways because they are

motivated by different factors and affected differ-

ently by policies. A company’s strategy or a food

policy on traceability must necessarily consider the

variability of consumers’ perception and how the

intention to purchase and the WTP for traceable

food differ across the segments.

This research is part of an EU funded project

(TRACE, Tracing the Origin of Food) aimed at

studying European consumer behaviour and percep-

tion on food traceability. Other papers have been

published based on the qualitative data collected

within this project; for instance, Giraud and Hala-

wany have studied the issue through a literature

review (Giraud & Halawany, 2006a) and focus group

analysis (Giraud & Halawany, 2006b). Results show,

on the one hand, consumer knowledge and impor-

tance associated with traceability as a tool to

guarantee food quality and safety and, on the other

hand, cross-cultural differences on consumers’ per-

ception of traceability. Van Rijswijk et al. (2008),

based on laddering interviews and means-end chain

analysis, have debated in more detail these cross-

national differences. Italian consumers, according to

this research, are more guided by food safety when

purchasing traceable food, while French consumers

are more involved in quality attributes such as

quality labels and indication of origin. Finally, van

Rijswijk and Frewer (2008) have shown the results of

a semi-structured interview analysis which confirm

the dichotomy between quality and safety issues

related to food traceability in different countries.

The current research departs from these results

and develops a quantitative study applying empiri-

cally and theoretically derived latent variables and

statistical market segmentation techniques to the

identification of groups of Italian consumers with

their own purchase behaviour and perceptions of

traceable food. Once the clusters have been defined,

group profiles are discussed with regard to their

attitude towards and beliefs about traceable chicken

and honey. Clusters are then compared for signifi-

cant differences in intention to purchase and WTP

for traceable food, as well as socio-demographic

characteristics. Conclusions provide insight in terms

of design and promotion of public and private

initiatives.

Methodology

Data collection and sampling

The perceived benefits of traceability were studied

by means of face-to-face, in-home interviews con-

ducted in Italy during November 2006, following a

pilot study conducted in May 2006 on 120 respon-

dents. The sampling unit was the household and the

respondent was the person responsible for the actual

purchase of food. Stratified cluster sampling with

systematic random selection of the sampling units

was applied. Specific locations and sample sizes in

each location were decided according to heteroge-

neity and variance criteria. The final sample consists

of 503 respondents (Table I).

The questionnaire was constructed largely using

multiple items based on one of the most influential

theories on the causal link between attitude and

behaviour: the theory of planned behaviour or TPB

(Ajzen, 1991). According to TPB, human behaviour

is guided by favourable or unfavourable attitudes, by

perceived pressure from social groups (subjective

norm (SN)) and by perceived presence of factors

facilitating or complicating the behaviour (perceived

behavioural control (PBC)). Intention is consid-

ered a good predictor of actual behaviour, and is

modelled as a combination of three dimensions of an

individual’s own beliefs: behavioural beliefs, norma-

tive beliefs and control beliefs. Behavioural beliefs are

considered as the likely outcome of an action, such as

good purchase, producing either positive or negative

attitude towards behaviour; normative beliefs refer

to social pressure from people or other social groups

affecting an individual’s opinion and beliefs (i.e.

family, media, etc.); and control beliefs refer to the

degree of control that the person feels if he or she has

over performing the behaviour (i.e. the likelihood of

finding the product at the supermarket will probably

affect the intention to purchase that good). In our

research, TPB was adopted and expanded according

to the specific nature of the context analysed. When

behaviour is habitual, as for food products, consumer

responses are often activated automatically, and

many studies have pointed to the importance of

habits in explaining repetitive behaviour, such as food

consumption (Honkanen et al., 2005; Verbeke &

Vackier, 2005). Moreover, the consumers’ difficulty

in making their own assessments of the risks related

to food hazards raises the importance of trust in

evaluating the information and safety standards

provided by the supply chain actors or public

authority (Lobb et al., 2007; Stefani et al., 2008).

We therefore introduced habits and trust into the

variables used for consumer segmentation. The

traceability-related concepts, as well as the variables

affecting traceable food choice, were derived from

Benefits of traceability in food markets 93

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qualitative research conducted through literature

review (Giraud & Halawany, 2006a), focus groups

(Giraud & Halawany, 2006b), means-end chain

analysis (Van Rijswijk et al., 2008), as well as semi-

structured interviews analysis (van Rijswijk &

Frewer, 2008) in six European countries.1

Chicken and honey were considered separately.

Since previous research has observed that cross-

national differences in traceability perception may

arise (Giraud & Halawany, 2006b; van Rijswijk

et al., 2008), respondents were provided with a

definition of traceable chicken and honey at the

beginning of the interview to avoid different back-

ground information.2 All the questions use seven-

point Likert scales where 1 indicates strong dis-

agreement and 7 indicates strong agreement. In

total, 258 questionnaires were submitted for chicken

and 245 for honey.

A comparison of the items means across chicken

and honey samples indicated some significant differ-

ences (Table II). Expected quality is higher for

traceable honey than for chicken (item Q9), while

intention to buy traceable chicken is significantly

higher compared to honey (Q25). The recent avian

flu scare may explain why consumers express a

stronger intention to purchase traceable chicken.

The results also indicate that consumers’ level of

trust in tracing back process is significantly higher

for chicken than for honey (Q22).

Data analysis3

An initial exploratory factor analysis (principal com-

ponents, varimax rotation, eigenvalues greater than

one) was performed separately on data from the

chicken and honey samples. This method is used to

explain variability among observed variables in terms

of fewer latent (unobserved) variables called factors.

The observed variables were modelled as linear

combinations of the factors. A varimax rotation was

Table I. Descriptive statistics of the sample data.

Chicken Honey Total

Percentage (%) Percentage (%) Percentage (%)

Gender

Males 27.3 24.6 26.0

Females 72.7 75.4 74.0

Age

18�24 13.7 11.5 12.6

25�40 32.0 29.1 30.6

41�50 28.1 21.7 25.0

51�60 13.3 20.1 16.6

�60 12.9 17.6 15.2

Education

Primary 8.6 9.4 9.0

Lower secondary 41.4 39.8 40.6

Upper secondary 26.2 26.2 26.2

Tertiary 23.8 24.6 24.2

Average family composition 3.0 2.9 2.9

Monthly family net income

B1000 Euro 10.9 11.9 11.4

1000�2000 Euro 40.6 39.8 40.2

2000�3000 Euro 22.7 20.1 21.4

�3000 Euro 8.6 8.6 8.6

Don’t know or don’t wish to disclose 17.2 19.6 18.4

Purchase frequency

Everyday or almost everyday 3.5 0.0

Several times a week 16.8 0.0

Once a week 29.3 0.0

Several times a month 34.0 4.9

Once a month 12.5 14.3

Every two months 3.9 11.9

Every three/four months 0.0 20.1

Twice per year 0.0 21.3

Once per year 0.0 27.5

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Table II. Means and standard deviations of the questionnaire items for chicken (n�258) and honey (n�245).

Chicken Honey

Item code Questionnaire items Mean Standard deviation Mean Standard deviation

When I buy chicken/honey I look for information aboutQ1 The producer [farmer/beekeeper] 4.63 2.03 4.71 2.01Q2 The production process 3.93 2.17 3.79 1.98Q3 The country and region of origin 5.32 1.90 5.26 1.82Q4 The existence of a certificate 4.77 1.97 4.53 2.05

Traceable chicken/honey, in comparison to other chicken/honey now available in the shops, will likely beQ5 Healthier 5.64 1.32 5.62 1.30Q6 Tastier 4.71 1.60 4.94 1.58Q7 Of known origin 6.05 1.06 5.93 1.20Q8 Safer 5.91 1.21 5.82 1.24Q9 Of more satisfying quality 5.45 1.33 5.71 1.17*Q10 Guaranteed for being controlled 6.03 1.14 6.09 0.97

I would buy traceable chicken/honey becauseQ11 Family, partner and friends approve 4.66 2.04 4.61 2.12Q12 Doctors and nutritionists are in favour 4.56 1.93 4.38 1.95Q13 Media are in favour 3.21 1.85 3.05 1.83Q14 Food industry and food supermarkets promote it 3.60 1.82 3.41 1.83Q15 People important to me buy this type of chicken/honey 3.92 2.05 4.11 2.02

Regarding the additional information about the production process and origin of traceable chicken/honeyQ16 It will be easy to look for it 4.98 1.62 4.81 1.63Q17 I will feel confident when looking for it 5.23 1.50 5.08 1.54Q18 I will look for it without help from others 5.42 1.62 5.27 1.62Q19 It will be easy to understand it 5.25 1.46 5.18 1.59Q20 I will be confident when understanding it 5.40 1.44 5.33 1.49Q21 I will understand it without help from others 5.39 1.59 5.34 1.59Q22 I believe traceable chicken/honey can be traced back 5.55 1.40 5.13 1.42**Q23 I trust the info provided about production process and origin 5.40 1.24 5.43 1.12Q24 If the info for traceable chicken/honey is certified I trust it to be genuine 5.71 1.26 5.78 1.08

Regarding your intention to buy traceable chicken/honey?Q25 I intend to buy it 5.88 1.25 5.63 1.35*Q26 I will search for it when I next go shopping for food 5.37 1.63 5.16 1.57Q27 It is important to buy it when I do my next food purchases 4.88 1.79 4.71 1.85Q28 Suppose the price of [chicken/honey] currently available in the supermarket is

[3.50 t/kg/3.70 t/500 g jars]. The price of the traced [chicken/honey] with theunique identity details and the additional available information that weintroduced will be higher than [3.50 t/kg/3.70 t/500 g jars], but is notdetermined yet. How much more, in percentage, would you be willing to pay topurchase such a [chicken/honey]?

19.16 24.13 18.65 22.76

*pB0.05; **pB0.001.

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necessary to enhance the interpretability of the scores

within the factors. The output of the analysis is a

standardised factor score associated with each record

of the original database (Harman, 1976).

The factors were used to disaggregate the main

sample into consumer segments with similar percep-

tion and behaviour towards, respectively, traceable

chicken and honey. From a marketing and research

perspective, segmentation is the process of defining

meaningful sub-groups of individuals or objects

that behave in similar ways or have similar needs,

generally by means of multivariate statistical analy-

sis. Respondents were clustered according to their

similarity in profiles defined by attitudinal, beha-

vioural or personality characteristics. The method

applied was Ward’s hierarchical cluster analysis

(Ward, 1963). Ward’s linkage is distinct from all

the other methods in that it uses an analysis of

variance approach to evaluate the distances between

clusters. The aim in Ward’s method is to join cases

into clusters such that the variance within a cluster is

minimised. To do this, clusters are then merged

in such a way as to reduce the variability within a

cluster. In short, this method attempts to minimise

the sum of squares (SS) of any two (hypothetical)

clusters that can be formed at each step. In general,

this method is regarded as very efficient, although it

yields smaller clusters.

This study applies a market segmentation analysis

based on latent variables and identifies a number of

segments profiled according to attitudes towards and

expectations from traceable food. Once the cluster

profiles have been discussed separately for the two

products, a comparison was made for significant

differences in the intention to purchase and WTP for

traceable food, as well as socio-demographic char-

acteristics. Intention, which in TPB is considered the

antecedent of behaviour, was measured by adding the

scores of three separate items with high internal

reliability (Q25, Q26 and Q27 in Table II).

Although there are several economic tools to value

non-market goods (such as hedonic pricing), con-

tingent valuation method (CVM) has been employed

in cost�benefit analysis for over 35 years and there are

over 2000 papers and studies dealing with

this topic (Carson, 2000). Applications of CVM to

estimate benefits include the case of food safety

policy issue (Buzby et al., 1995; Caswell, 1995;

Henson, 1996, 1997). Contingent valuation (CV) is

a flexible tool which can be adapted to analyse

specific food safety policies; its results are compar-

able in terms of accuracy to analogous studies using

other approaches to evaluating non-market goods.

Moreover, it does not rely on secondary data sources

and, at the same time, tends to be less expensive than

market experiments (Carson, 2000). However, as

several authors have noted, CV’s reliance on con-

sumers’ subjective responses makes the results vul-

nerable to several potential biases: first of all, elicited

premium is higher than what consumer would

actually pay as long as hypothetical scenarios are

considered (Carson, 1997). Secondly, CV surveys

that value food safety typically include information

on different levels of food risk; however, many studies

have shown that consumers have difficulty in under-

standing and processing risk information4 (Buzby

et al., 1995; Mora, 1998).

In order to implement the CVM, it was necessary

to select an elicitation procedure for the survey.

Three elicitation techniques are commonly used in

CV market simulations: iterative bidding, payment

cards (PCs) and dichotomous choice (Boyle &

Bishop, 1988). The results reveal that no single CV

technique is neutral and each technique has its

strengths and weaknesses (Carson, 1997). For

instance, the iterative bidding estimates imply a

starting point bias, while the PC and dichotomous

choice estimates are influenced by the interviewers

soliciting the contingent values.

The survey incorporated the PC elicitation meth-

ods (Boyle & Bishop, 1988; Buzby et al., 1995). The

PC method was selected for its simplicity and its

ability to obtain precise WTP estimates minimising,

at the same time, the starting point bias. The PC

methods ask respondents to select the amount that

they are willing to pay from a check list of payment

amounts above the average price of a common

product. In our study, the PC provided consumers

with a list of payment amounts for traceable chicken

and honey above an average price.5 The prices were

expressed in t/kg for chicken and t/500 g jar for

honey; next to the monetary amount, the relative

increase above the average price was reported (in

percentage). Respondents were provided with space

below the PC to allow them to answer with WTP

values not shown in the column of numbers; they

were also informed that circling zero meant that they

would not pay more to buy traceable products.

Finally, a simple mean was calculated of all WTP

values, since no uncompleted surveys and non-

responses were found (Table II, item Q28).

Results for chicken

Factor analysis for chicken

The exploratory factor analysis, performed on 24

observed variables (items) extracted from the ques-

tionnaire (Table II, from item Q1 to Q24), yielded six

factors accounting for about 70% of the total variance

of the respective observed variables (Table III). The

reduction in the number of variables from 24 to

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Table III. Factor analysis: Cronbach coefficient alpha, factor description with correlated questionnaire items (observed variables) and total explained variance for chicken.

Rotation sums of squared loadings

Factor Name Alpha Description Items Total

Variance

percentage (%)

Variance percentage

(%) cum.

1 Perceived behavioural

control (PBC)

0.89 It will be easy looking for the traceable chicken, confident when doing

without help from others. It will be easy understanding the information

about the traceable chicken, confident when doing without help from

others.

Q16

Q17

Q18

Q19

Q20

Q21

3.93 16.4 16.4

2 Subjective norm (SN) 0.82 Would buy this chicken because family, partner and friends approve,

doctors and nutritionists are in favour, media are in favour, food industry

and food supermarkets promote it, people important to me buy this type

of chicken.

Q11

Q12

Q13

Q14

Q15

3.04 12.7 29.0

3 Habit 0.83 When I buy chicken I look for info about the producer (farmer) of the

chicken, the production process, the country of origin and the existence

of a certificate proving such information.

Q1

Q2

Q3

Q4

2.82 11.7 40.8

4 Attitude control 0.85 The traceable chicken, in comparison to other chicken available in the

shops, will likely be of known origin, safer, guaranteed for being

controlled.

Q7

Q8

Q10

2.77 11.5 52.3

5 Trust 0.81 I believe this chicken can be traced back, I trust the info provided about

production process and origin. I trust it to be genuine if the info for this

chicken would be certified.

Q22

Q23

Q24

2.26 9.4 61.7

6 Attitude quality 0.73 The traceable chicken, in comparison to other chicken available in the

shops, will likely be healthier, tastier, of more satisfying quality.

Q5

Q6

Q9

1.81 7.5 69.3

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6 greatly simplified further analysis, although there

was a loss of information of about 30% of the overall

variance. The results of the rotated components

matrix are shown in Table III; the description of

each factor takes into account the correlations

between factors and each observed variable larger

than 0.50. For simplicity, the data are not shown in

the table.

The 6 factors are interpreted, respectively, as

PBC, accounting for 16.4% of the total variance,

SN accounting for 12.7%, habit accounting for

11.7%, then attitude towards control (11.5%), trust

(9.4%) and attitude towards quality (7.5%). Note

that attitude is split in two different factors, the first

covering the knowledge of origin, safeness and

control guarantees, whilst the second refers to

more qualitative aspects of the traceable chicken

such as taste, health effects and satisfying quality.

This distinction reflects the two safety and quality

dimensions of traceability perceived by consumers,

as discussed by other authors (van Rijswijk &

Frewer, 2008; van Rijswijk et al., 2008). In any

case, the internal reliability of the factors is satis-

factory, as measured by Cronbach coefficient alpha

(Table III).6

Respondents were willing to pay t4.17 per kg,

equal to a 19.2% premium for traceable chicken

compared to the product now available in super-

markets with base price of 3.50 t/kg. In order to

show the relation between the 6 factors, WTP and

intention to purchase traceable food, Pearson’s

correlation coefficient was calculated (Table IV).

Not surprisingly, WTP for traceable chicken is

slightly positively correlated to consumer attitude

and trust. Our analysis showed a positive correlation

of 0.13 for attitude control, 0.16 for trust and 0.13

for attitude quality (all significantly different from

zero at pB0.05). This means that more favourable

attitude towards traceable chicken and trust in

related information lead to higher WTP. At the

same time, intention is related to attitude, PBC,

trust and habit. The following cluster analysis aims

to identify how these factors are segmented across

the population and whether it is possible to identify

specific groups with different relationships between

attributes and behaviour patterns.

Cluster analysis for chicken

The factors obtained were entered into a cluster

analysis using the hierarchical Ward method, which

gave a reasonable result of five groups of consumers.

The mean scores for each cluster of the selected

variables are shown in Table V. Given that the

variables analysed are standardised (mean�0, stan-

dard deviation�1), the scores of the 6 variables for

each cluster have to be compared to the mean value

of the sample, which is zero. So, for instance, if

‘‘Trust’’ scores �0.52 for cluster 1, those consumers

trust traceable chicken less than the mean of the

sample.

The clusters were named as Uninterested consu-

mers, Fast Purchasers, Sceptical, Well-disposed and

Trusting. For each segment, the socio-demographic

composition was investigated and the WTP and

intention to purchase calculated and compared to

evaluate any difference between the segment value

and the mean value of the overall sample. The results

also suggested that socio-demographic characteris-

tics do not statistically differ across the segments,

except age. This means that respondent age is the

only socio-demographic characteristic, among those

investigated,7 which influences perception of trace-

able chicken purchase.

Uninterested consumers account for more than one-

fourth of the sample and show a relatively low interest

in control of the traceable chicken and a low level

of trust but, at the same time, high PBC. These

consumers feel confident in finding and understand-

ing the information provided with traceable chicken,

although they are unfavourable to its safety and

guarantees, and mistrust the effective application of

the traceability system (tracing back process, truth of

the information provided). This translates into a

WTP of about 8%, which is significantly lower

than the sample mean value (at pB0.001), and into

a lower intention to purchase traceable chicken

(intention score 14.4, significantly different to the

sample mean at pB0.001). Uninterested consumers

are mostly older people (over 45).

Fast Purchasers, covering 20% of the sample, group

mostly young people (42% of the segment are under

30). They are not used to looking for information

about a product (very low average score for habit),

and are not able to look for and understand

Table IV. Pearson’s correlation coefficient and significance (two-tailed), chicken.

PBC SN Habit Attitude control Trust Attitude quality

Willingness to pay for traceable chicken �0.03 0.04 0.08 0.13* 0.16* 0.13*

Intention to purchase traceable chicken 0.32** 0.03 0.15* 0.33** 0.25** 0.30**

Note: Significantly different from zero at *pB0.05, **pB0.01.

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additional information about traceable chicken.

However, they show a favourable attitude towards

the product and trust the system, expressing a WTP

of 23% above the average price which is higher than

mean WTP, although not significantly. Their inten-

tion to purchase traceable chicken is not significantly

different from the sample mean value.

Sceptical consumers are mostly older people with

low PBC, low trust and low attitude towards quality

of traceable chicken. In this case, the higher age (56%

are over 50) may lead to a general perception of

difficulty in looking for and understanding additional

information on traceable chicken. This generates a

WTP slightly lower than the mean and an inten-

tion to purchase the product significantly lower

compared to the sample mean (pB0.05).

Cluster 4 is composed of Well-disposed consumers

used to looking for information, confident in doing

so, trusting and favourably disposed towards trace-

ability. Their behaviour and attitude are positively

influenced by third parties, especially family mem-

bers and doctors or specialists. So their WTP

(25.6%) is higher than mean, although not signifi-

cantly, and their intention score is the highest of the

sample (18.7, pB0.001).

Trusting consumers, who are not influenced by

other subjects, strongly believe the effectiveness of

traceability, showing a favourable attitude towards

controls and a positive assessment of their ability to

look for and understand traceability information.

Their WTP is also positive, but not significantly

higher than mean; on the other hand, their intention

to purchase is significantly higher than the mean

(score 18.5, pB0.001).

Results for honey

Factor analysis for honey

The factor analysis was performed on the same

variables as for chicken and used the same theoretical

framework, TPB, for the identification of the factors

influencing traceable honey purchase. In this case

too, the result was a six factor output accounting for

66% of the total variance of the observed variables

(Table VI).

The factors are very similar to those found for

traceable chicken, which demonstrates satisfactory

robustness of the model. They are PBC, accounting

for 15.6% of the total variance, SN accounting for

12.6%, habit (11.2%), attitude towards intrinsic

quality (10%), attitude towards system quality

(9%) and trust (7.8%). The attitude concept is

again split into two different factors, although for a

different reason. For honey, the first factor is more

oriented to the intrinsic quality of the product

(healthier, tastier, of known origin, safer) and the

second more to the production system quality (more

Table V. Cluster analysis: average scores for each cluster and variables for chicken.

Uninterested Fast Purchasers Sceptical Well-disposed Trusting

n�67 n�50 n�50 n�50 n�41

Variables 26.0% 19.4% 19.4% 19.4% 15.9%

PBC 0.43*** �0.62*** �0.87*** 0.46*** 0.56***

SN 0.11 0.34** �0.30* 0.81*** �1.21***

Habit �0.24* �0.97*** 0.36** 0.73*** 0.25

Attitude control �0.99*** 0.25* 0.39** 0.30*** 0.47***

Trust �0.52*** 0.31* �0.65*** 0.43*** 0.75***

Attitude quality 0.00 0.35* �0.64*** 0.29*** 0.01

WTPa (%)

Mean 8.2*** 23.2 18.6 25.5 25.0

Standard deviation 9.7 27.3 23.6 28.6 26.1

Median 5.0 15.0 10.0 15.0 15.0

Minimum 0.0 0.0 0.0 0.0 0.0

Maximum 50.0 100.0 100.0 100.0 100.0

Intentionb

Mean 14.4*** 15.6 14.5* 18.7*** 18.5***

Standard deviation 3.7 4.2 4.6 4.3 3.2

Median 15.0 16.0 15.0 18.0 20.0

Minimum 6.0 3.0 3.0 14.0 9.0

Maximum 21.0 21.0 21.0 21.0 21.0

*Significant pB0.05; **significant pB0.01; ***significant pB0.001.aSample mean�19.2; median�10.0; standard deviation�24.1; minimum value 0.0, maximum value 100.0.bSample mean�16.1; median�17.0; standard deviation�4.1; minimum value 3.0, maximum value 21.0.

Benefits of traceability in food markets 99

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Table VI. Factor analysis: Cronbach coefficient alpha, factor description with correlated questionnaire items (observed variables) and total explained variance for honey.

Rotation sums of squared loadings

Factor Name Alpha Description Items Total

Variance

percentage (%)

Variance percentage

(%) cum

1 Perceived behavioural

control (PBC)

0.87 It will be easy looking for the traceable honey, confident when doing

without help from others. It will be easy understanding the information

about the traceable honey, confident when doing without help from

others.

Q16

Q17

Q18

Q19

Q20

Q21

3.75 15.6 15.6

2 Subjective norm (SN) 0.81 I would buy this honey because family, partner and friends approve,

doctors and nutritionists are in favour, media are in favour, food industry

and food supermarkets promote it, people important to me buy this type

of honey.

Q11

Q12

Q13

Q14

Q15

3.02 12.6 28.2

3 Habit 0.81 When I buy honey I look for info about the producer (beekeeper) of the

honey, the production process, the country of origin and the existence of

a certificate proving such information.

Q1

Q2

Q3

Q4

2.70 11.2 39.5

4 Attitude intrinsic quality 0.74 The traceable honey, in comparison to other honey available in the

shops, will likely be healthier, tastier, of known origin and safer.

Q5

Q6

Q7

Q8

2.41 10.0 49.5

5 Attitude system

quality

0.69 The traceable honey, in comparison to other honey available in the

shops, will likely be of more satisfying quality, guaranteed for being

controlled.

Q9

Q10

2.15 9.0 58.5

6 Trust 0.70 I believe this honey can be traced back, I trust the info provided about

production process and origin, I trust it to be genuine if the info for this

honey would be certified.

Q22

Q23

Q24

1.87 7.8 66.2

100

C.

Mora

and

D.

Men

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satisfying quality, control guarantee). In the case of

honey too, the Cronbach coefficient alpha shows a

satisfactory internal reliability of the factors.

Compared to base honey price, t3.70 per 500 g

jar, the mean WTP was 4.39 t/500 g jar, resulting in

a 18.7% premium. The correlations of the factors

with WTP and intention to purchase traceable honey

are shown in Table VII.

WTP for traceable honey is correlated with

consumer attitude towards system quality (0.23,

pB0.01) and with habit (0.14, pB0.05). Intention

to purchase is also related to attitude towards

quality, trust, habit and PBC. The following cluster

analysis evaluates whether these relations are main-

tained across different groups of consumers.

Cluster analysis for honey

The hierarchical cluster analysis resulted in five

segments of consumers. Table VIII shows the scores

of the variables for each cluster. In this case, the

population is segmented into five different clusters:

Influenced, Not Confident, Familiar, Sceptical and Well-

disposed towards traceable honey. The socio-demo-

graphic composition of the clusters was also investi-

gated, and the WTP and intention to purchase the

product compared across the segments. In the case of

honey, WTP did not differ significantly across the

clusters, although there were some differences (Table

VIII). On the other hand, intention to purchase is

significantly higher for the Well-disposed consu-

mers. Age and gender differ significantly across the

segments, proving that some personal characteristics

can in fact influence attitude and behaviour.

Influenced consumers are those not used to looking

for information about the production process and

the origin of honey. This segment is strongly

influenced in the purchase decision by family mem-

bers and specialists. As shown in Table VIII, the

WTP of influenced consumers is lower than the

Table VII. Pearson’s correlation coefficient and significance (two-tailed), honey.

PBC SN Habit Attitude intrinsic quality Attitude system quality Trust

Willingness to pay for traceable chicken 0.03 �0.10 0.14* �0.08 0.23** 0.06

Intention to purchase traceable chicken 0.15* 0.11 0.24** 0.25** 0.42** 0.19**

Note: Significantly different from zero at *pB0.05, **pB0.01.

Table VIII. Cluster analysis: average scores for each cluster and variables for honey.

Influenced Not Confident Familiar Sceptical Well-disposed

n�44 n�52 n�72 n�35 n�42

Variables 18.0% 21.2% 29.4% 14.3% 17.1%

PBC �0.08 �0.16*** 0.34*** 0.49*** 0.53***

SN 0.84*** �0.52*** 0.62*** �0.92*** �0.54***

Habit �1.31*** 0.21 0.53*** �0.27 0.42***

Attitude intrinsic quality 0.05 �0.38** 0.05 �0.14 0.45***

Attitude system quality �0.02 0.32 �0.45*** �0.06 0.45***

Trust 0.17 0.10 �0.04 �1.27*** 0.83***

WTPa(%)

Mean 13.9 20.3 18.9 17.6 22.5

Standard deviation 18.1 24.9 22.8 20.6 25.8

Median 10.0 12.5 10.0 10.0 15.0

Minimum 0.0 0.0 0.0 0.0 0.0

Maximum 70.0 100.0 100.0 70.0 100.0

Intentionb

Mean 14.4 15.4 15.7 14.4 17.4***

Standard deviation 4.6 4.6 3.5 4.1 3.8

Median 14.5 16.0 17.0 15.0 18.0

Minimum 3.0 3.0 6.0 7.0 7.0

Maximum 21.0 21.0 21.0 21.0 21.0

*Significant pB0.05; **significant pB0.01; ***significant pB0.001.aSample mean�18.7; median�10.0; standard deviation�22.8; minimum value 0.0, maximum value 100.0.bSample mean�15.5; median�16.0; standard deviation�4.2; minimum value 3.0, maximum value 21.0.

Benefits of traceability in food markets 101

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mean WTP for the sample (13.9%), although not

significantly. The segment is characterised by the

incidence of women (84%) over 45 (70%).

The Not Confident consumer displays a low level of

confidence in looking for traceable honey and

understanding the information, and a low depen-

dence on the approval of third persons. Consumers

in this segment do not believe that traceable might

be tastier or healthier than conventional honey, as

demonstrated by the negative value of attitude

towards intrinsic quality. Young women mostly fall

into this cluster.

The largest segment comprises consumers familiar

with looking for information about the origin of

honey in terms of beekeepers and production pro-

cess. Familiar consumers, strongly influenced by

family members, doctors and specialists, feel con-

fident in finding and understanding additional in-

formation; however, they are not so convinced that

traceable honey is of better quality or better guaran-

teed than other kinds of honey. The WTP and

intention to purchase are, thus, in line with the

mean values.

Sceptical consumers, representing only 14% of the

sample, are very independent and not affected in

their behaviour by others’ opinion, especially not by

the media, and believe they can find and understand

the additional information. At the same time, these

consumers, mostly young people, do not believe in

the possibility of recalling traceable honey and

mistrust traceability information.

The segment of Well-disposed consumers, account-

ing for 17% of the sample and comprising a big share

of young people (36% are under 30), show high level

of trust in the information provided and in the

correct functioning of traceability system (i.e. the

possibility of tracing back the product). This cluster

displays a favourable attitude towards both intrinsic

and system dimension of quality too. Well-disposed

consumers perceive that they are able to look for and

understand the information provided without any

help, and they are not affected by the media,

businesses, family members and other people in

their decision to purchase. This favourable attitude

and trust results in a high intention to purchase

traceable honey compared to the other groups

(intention score 17.4, pB0.001), and a WTP which

is higher, although not significantly, than the mean

WTP.

Discussion

In the literature, two different dimensions of con-

sumer perception of traceability are discussed. Con-

sumers perceive the usefulness of traceability in

having more information on a food product, espe-

cially on the region or country of origin, and they

also perceive the possibility of withdrawal of a

defective product (van Rijswijk & Frewer, 2008;

van Rijswijk et al., 2008).

Indeed, traceability systems can perform an im-

portant economic and social function in limiting the

costs and risks of a food safety problem for society by

allocating the responsibility over the food chain and

allowing it to react rapidly. This improved level of

food safety increases consumers’ WTP for the (safer)

product (Pouliot & Sumner, 2008). At the same

time, it is questionable if traceability can also reduce

information asymmetry in providing consumers with

quality information. Hobbs et al. (2005) suggest that

traceability itself does not address the issue of

consumer information asymmetry with respect to

credence quality attributes, and that its implementa-

tion might be a necessary but not sufficient condi-

tion for ex ante verification of quality attributes. This

has been also confirmed by other studies on beef

traceability (Mora & Menozzi, 2006; Verbeke &

Ward, 2006; Angulo & Gil, 2007; Loureiro &

Umberger, 2007).

So consumers may evaluate traceability in a

bundle of attributes. Some studies have suggested

that those consumers who relate traceability to more

than a labelling of origin, and associate it with the

knowledge of production procedures or additional

assurances, are willing to pay a price premium for it

(Hobbs et al., 2005; Angulo & Gil, 2007; Loureiro &

Umberger, 2007; Lichtemberg et al., 2008).

This paper has investigated the perception and

attitude of Italian consumers towards traceable food

products. The aim was two-fold; on one hand, to

empirically identify segments of consumers clustered

with similar attitudinal�behavioural characteristics

and perception of traceability and, on the other

hand, to analyse the different levels of WTP and

intention to purchase traceable products across

consumer segments. The TPB was used as a theore-

tical framework to identify main attitude�behavioural

relations; scores on factor analysed multi-dimen-

sional concepts were used to segment consumers

into potential groups with different profiles of atti-

tude, perception, trust and habits with regards to

food traceability.

We found almost the same WTP value for trace-

able honey and chicken (19.2% and 18.7%, respec-

tively),8 possibly because consumers gave traceability

the same value. This result implies there is little point

in addressing different WTP for traceability across

products; instead, further investigation with cluster

analysis confirms the variability of WTP across

different segments of consumers with different pro-

files of attitude, perception, trust and habit.

102 C. Mora and D. Menozzi

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The analysis showed that consumer attitude

correlates positively with WTP both for chicken

and honey. In other words, the more favourable the

attitude of the consumer towards traceable products,

the higher the WTP. Moreover, trust in traceability

scheme and provided information leads to higher

WTP for chicken, and habit is positively related

to traceable honey WTP. The stronger the habit of

looking for additional information on producer,

origin and production process, the higher the pre-

mium the consumer would pay for traceable honey.

The segmentation conducted for both case stu-

dies revealed the unique characteristics driving the

behaviour of each segment. Uninterested consumers

are those less willing to pay for traceable chicken

who do not believe that traceable chicken is more

stringently controlled or that it can be traced back

if necessary, although they are confident in their

own ability to find and understand the traceability

information. These consumers are not interested

in product traceability and perceive no difference

between traceable and conventional chicken. In

other words, chicken is perceived as a commodity.

On the contrary, Well-disposed and Trusting consu-

mers, 36% of the sample, are more willing to pay,

although not significantly, and have stronger inten-

tion to purchase traceable chicken.

The stronger intention to purchase traceable

honey was present among Well-disposed consumers,

who also exhibit a WTP slightly higher than the

mean. However, this group accounts for only 17% of

the population. This lower level of interest in trace-

able honey may be in part explained by the limited

amount of honey consumed in Italy.

This paper has suggested that those consumers

who trust traceability to improve food safety by

means of trace back procedures have a stronger

intention and show a greater WTP for traceable

foods. Moreover, some segments perceive traceability

as a tool providing insight on quality attributes like

safety, origin, health; this perception may also make

them willing to pay a price premium for traceable

food products. But these consumers represent only a

small share of the sample, especially for honey. For

many consumers, traceability alone does little to

reduce information asymmetry with respect to qual-

ity attributes.

It is important to note that the CVM applied in

this paper is open to a number of criticisms (Carson,

1997; Rigby et al., 2004). The underlying weakness

is that the study is of hypothetical nature, subject to a

number of hypothetical bias; moreover, Carson

(1997) suggests that the hypothesis of scope insensi-

tivity is generally rejected (‘‘embedding’’). As noted

by Rigby et al. (2004), other specific problems with

CVM may include strategic bias, instrument bias,

starting point bias, desirability bias, the closely

related yea-saying and, on the other hand, protest

votes or protest zeros.

The authors consider several of these key areas of

the debate about CVM and conclude that many of

the problems with CVM can be resolved by careful

study design and implementation (Carson, 2000).

An alternative to CVM as a way to eliciting con-

sumers’ WTP for non-market goods is the conjoint

analysis or choice-based experiments (Carlsson &

Martinsson, 2001; Goldberg & Roosen, 2007). A

priority for future researches is a conjoint study to

compare results obtained from each method. In this

latter case, it would be possible to consider different

attributes related to traceability such as origin,

quality certification, production information, etc.

evaluating the utility associated by consumers to

each. Indeed, several authors (Hobbs et al., 2005;

Angulo & Gil, 2007) have suggested that traceability

should be combined with quality guarantee schemes,

such as PDO (Protected Designation of Origin), PGI

(Protected Geographical Indication), animal welfare

etc. in order to maximise its effectiveness. In cases

where traceability is well communicated and under-

stood, it may even raise expectations of product

quality (Verbeke & Ward, 2006).

The findings of our research have shown that

private or public initiatives to promote traceable

food schemes need to be targeted to consider the

differences between different consumer segments.

The real value of segmentation is that it can

be translated into achievable strategies in that the

information is useful to identify and target the most

effective interventions. The first point to be con-

sidered is that intervention should focus on those

segments with the greatest potential to increase their

purchase of traceable products; the favourable atti-

tude and the expectation of food quality and safety

can be considered as predictive indicators of the

more sensitive segments. Secondly, it appears not to

be worth trying to encourage consumers who are not

currently used to looking for additional information

and have no intention of purchasing traceable foods

in the future. The economic and communication

efforts need to be targeted on the most receptive

segments.

Acknowledgements

The authors gratefully acknowledge the anonymous

referees for many helpful comments. The question-

naire and the data collection were performed within

the EU financed TRACE Project (Tracing the origin

of food-contract CT-2005-006942) by the research

team on ‘‘Consumer behaviour’’ (WP7) led by Prof.

George Chryssochoidis of the Athens University of

Benefits of traceability in food markets 103

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Agriculture. The staff of the research team was

composed of C. Bauer and B. Schaer (EcoZept

Freising, Germany), P. de Carlos, J. Briz, and I. de

Felipe (Universitad Politecnica de Madrid, Spain),

L. Frewer and W. van Rijswijk (Wageningen Uni-

versity, The Netherlands), C. Mora, D. Menozzi and

G. Faioli (Universita degli Studi di Parma, Italy), C.

Amblard, R. Halawany and G. Giraud (ENITAC,

France), P. Chrysochou, G. Chryssochoidis and O.

Kehagia (Athens Agricultural University, Greece).

We are also grateful to the European Commission for

financing and to the WP7 team for the effective

collaboration. However, the analysis performed and

the opinions expressed in the paper reflect the point

of view of only the authors.

Notes

1. The countries involved into the TRACE project were Italy,

Germany, the Netherlands, Spain, France and Greece. In this

paper we report the results of the Italian sample only.

2. Traceable chicken was defined as ‘‘a chicken for which unique

details are available by which it can be identified. For example,

information is available about its producer, the production

process of the chicken (e.g. feed, rearing conditions, treat-

ments), country and region of origin and a certification that

this information can be trusted. This chicken can be traced

back to the specific farm on which it was raised’’. Traceable

honey was defined in the questionnaire as ‘‘a honey for which

unique details are available by which it can be identified. For

example, information is available about its producer, the

production process of the honey (e.g. water content, if it

includes pollen, addition of various sugars, heating tempera-

ture, mixing with other honey), country and region of origin

and certification that this information can be trusted. This

honey can be traced back to its producer (beekeeper)’’.

3. The software used for the statistical analysis was SPSS 17.0.

4. All food-borne risk factors fall into the experience food

attributes (e.g. salmonellosis) and credence food attributes

(e.g. pesticide residues, food additives). In these situations

consumers rely upon external risk indicators to indicate the

level of food safety.

5. An average price of t3.50 per kg for chicken and t3.70 per 500

g jar for honey was considered. The data were derived from a

direct survey in modern and traditional distribution outlets.

6. In general, values of the coefficient alpha larger than 0.70 are

recommended, and a value between 0.80 and 0.90 indicates a

very satisfactory internal cohesion of the group (Ho Yu, 2001).

7. The other socio-demographic variables considered were gen-

der, family composition (number of family members and

presence of children), occupation, education, purchase fre-

quency and net family income.

8. An independent sample t-test has shown that the difference in

WTP for chicken and honey is not significant at the 0.05 level.

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