Evaluating trust in organic quality marks: a network approach using laddering data
Danilo Gambelli, Simona Naspetti
Key words: trust-building, laddering, network analysis, consumer behaviour
Abstract
A low level of information affects trust in organic quality in Italy. Since organic brands and labels credibility, depends on trust relationships that consumers perceive, it is crucial to understand which kind of relations are more relevant and which of them could have a positive or negative effect in the long-term. The purpose of this study is to examine trust relationship related to buying organic products, to better understand the consumer decision-making process and trust-builders inside the organic channel, using an innovative network approach based on laddering analysis. 
Introduction
Marks as forms of quality signals to consumers – previously analysed using Means-end chain theory and laddering data (Naspetti and Zanoli, 2005), are investigated using a Means End Chain (MEC) approach (Reynolds and Gutman, 1988). The study examines over 2124 sentences, structured into 56 codes, or variables, and attempts to determine how trust is built and how it affects other relationship outcomes (Zanoli et al., 2004). The relationships among consumers concepts are analysed using a social network approach (SNA) (Wassermann and Faust, 1994, and Bagozzi et al., 1996, for a SNA application to MEC), aiming to individuate sets of closely related concepts and to assess their relative importance in the consumers’ trust cognitive process. Switching from a qualitative study to a quantitative one improved the comprehension of means-end relations and shed a light on the antecedents and/or consequences of trust in organic quality signs.
Materials and methods
The objective of MEC theory is to understand what makes products personally relevant to consumers by modelling the perceived relationships between a product (defined as a bundle of attributes) and the consumer herself (regarded as holder of values). Naspetti and Zanoli, (2005) extend the notion of means-end chain structures of consumers’ product knowledge to symbolic signs such as quality marks. They used the soft-laddering technique, which is used to construct MEC, in order to identify relations between consumers and products mediated by the benefits offered from any hypothetical organic quality mark. This methodology has been developed as a tool to discover what personally motivates consumers to have trust in organic products. Approximately 104 in-depth interviews have been carried out in Italy as part of a larger EU-funded project (OMIARD). In order to identify their cognitive structures, consumers were asked to imagine a quality sign, that would give them confidence in the organic origin of the product and to link these motivations to product attributes (A) and their consequences (C) in order to reveal their underlying beliefs, feelings and desired ends (Values – V). MecAnalyst+ package was used to code consumers interviews, to derive the implication matrices and the relevant Hierarchical Value Maps (HVMs). The implication matrix – a square matrix with a size reflecting the number of elements mentioned by the respondents (56) – which reports the frequency of the direct connections between single categories (A, C, V, referred to as nodes in what follows), was used as an adjaciency matrix, which is the basis for the social network approach. In particular the laddering outcome matrix may be interpreted as a set of relational data. Relations are not the properties of nodes but of system of nodes; these relations connect pairs of nodes into larger relational system. The qualitative information raised by the laddering technique may be analysed via quantitative and statistical counts of relations. The adjacency matrix out-coming from the laddering is a valued and not symmetric one, the values being the absolute frequencies of relations occurring between each pair of nodes. The main interests for the purpose of data interpretation are: a)the individuation of the nodes that show the highest “importance” within the network, and b) the individuation of significant set of nodes that may eventually be considered as a structured subset of nodes within the general network of connection between A, C, and V. Concerning the first aspect, standard network centrality measures have been used: degree centrality, betweenness centrality and closeness centrality. The degree of a node is the number of nodes to which a node is directly connected. Degree centrality is a measure of “local centrality” as is a measure of how well a node is connected within its local environment. Beside degree centrality, betweenness centrality provides information about the extent to which a particular node lies between other nodes: in fact nodes with low degree centrality may however play a crucial role in connecting different subsets of nodes, hence playing an important “bridging” and role within the network. Betweenness centrality requires however the network to be symmetric, hence considers only the presence of a connection between two nodes regardless the direction of relations. The issue of laddering data symmetrisation is a delicate one and has been considered in (see Geiskens et al., 1998). For our purposes (i.e. individuate the “role position” of nodes arising from laddering analysis) indicators hinging upon the assumption of relational symmetry may provide useful information as well, integrating the results based on original directed data. Finally closeness centrality is an indicator of how close a node is with respect to all other nodes; it is therefore a measure of “global centrality”, and a node can be considered as central if it lies within short distance from many other points, the distance being defined as the length of the shortest path connecting nodes. In this paper is used to check for the actual hierarchic structure of the network. Concerning the second aspect, we have investigated the network using cliques analysis. Weak cliques analysis (i.e. a clique analysis ignoring the direction of links among nodes, have been used to individuate subset of nodes where every possible pair of nodes is directly connected, and such that these subset are not contained by any other clique. The concept of clique considers therefore group of nodes very strictly interconnected within the wider network. The network of laddering relationships has proved to be quite an interconnected one, and we decided to consider only cliques formed by at least 6 elements (quite a restrictive condition). All nodes indicated in table 1 are included in one or more cliques, hence confirming their active role in the choice process for organic products. We have tried to aggregate the cliques according to the nature of the nodes, and have accordingly defined labels that summarise their characteristics.

Results
SNA computation have been obtained with UCINET 6. Results for centrality measures are indicated in Tab. 1; in particular the node “have trust” assumes absolutely the most relevant position within the network of relations investigated. Results from closeness centrality analysis, not included in Tab 1 for space purposes, have shown that out degree closeness centrality is higher for attributes, (i.e. for nodes that are at the basis of the hierarchic structure of laddering), and lower for consequences and values (i.e. for nodes that are at the top of the hierarchy), and vice versa for in degree closeness, hence confirming the actual vertical integration of the network in terms of attributes, consequences and values. Such a result can be partially confirmed by the proportionally higher scores of out-degree centrality for attributes, and in degree centrality for values. 
Tab. 1: Centrality measures: nodes with higher scores 
		Degree centrality	Between centrality	
		out	in		
strict controls	A	√	√		
Info about place of origin	A	√			
info about label/control/standards	A	√		√	
Info about product			√	√	
Have trust	C	√	√	√	
feel relaxed	C	√			
avoid worries/feel safe	C		√		
own health	V		√		
In Tab. 2 a summary of results from clique analysis is indicated, showing five main clusters of attributes, consequences and values variously organised as a clique of at least 6 components, and labelled according to their characteristics: the clique group labelled “Reassurance” is the one including the higher number of nodes. In other words concepts linked with the Reassurance issue are those more strictly interrelated within the more general network, hence forming a much cohesive subsystem and showing that a wide amount of the trust building process organic quality signs is much connected with such an aspect. This is particularly relevant as the number of nodes that could be classified as belonging to the “Reassurance” concept is not over-represented in our database. Again, the clique clusters confirm the hierarchic structure of the network, with an increase of abstractness of concept attributed from clique group 1 to 5.
Tab. 2: Cliques clusters and node aggregation by clique membership
Clique group label	n. of cliques	Total n. of nodes included for each group label	
Organic label	2	12	
Look for health	3	18	
Reassurance	8	48	
Serenity	6	36	
Feel good	1	6	

Discussion
The results from SNA, substantially confirm the goodness of the qualitative MEC study hereby analysed, particularly the coding procedure, and the “vertical” structure of the model. The centrality of the “have trust” node was not unexpected (Tab.1). Firstly, because the cognitive study (MEC analyses) on quality signs shows the same unique nodal point (have trust), secondly, and more relevantly, because when deciding whether or not to buy organic products consumers usually mention the trust issue (Zanoli, 2004). The presence of a lot of aspect connected with the types of information to be placed on the label (tab.2) was also confirmed by the previous MEC study (Naspetti and Zanoli, 2005). The clique analysis has been particularly helpful in the individuation of subgroups of strictly connected nodes; furthermore these are organised in homogeneous groups according to the nature of the nodes they include. The resulting classification helps from one side to understand more clearly the essential factors affecting the trust building process, and may be a basis for a re-definition of more focussed analysis.
Conclusions
Network analysis can provide valuable support in interpreting and integrating information arising from laddering techniques, particularly in measuring in a consistent mathematic approach actual importance and role of different aspects (nodes) included in the laddering analysis. SNA may be considered as an useful diagnostic tool for standard MEC analysis and the integration between the two approaches shows a potential for further in depth analysis in the cognitive aspects of consumers behaviour. Since SNA emphasise the network nature of the cognitive structure and the role of single nodes and different types of relations between nodes, it is a valuable tool for implementing benefit-based market segmentation. 
Acknowledgment
The authors kindly acknowledge funding from the EU - Commission within the project “Organic Market Initiatives and Rural Development”(OMIaRD). The paper is the sole responsibility of the authors and do not necessarily reflect the EU - Commission view.
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