The Organic Food Market: Opportunities and Challenges - Organic Food and Agriculture: New Trends and Developments in the Social Sciences - page 79

convenience stores (1.94), and home delivery (3.27). Sporadic OF consumers, or SOF, are
consumers who do not buy OF on a regular base. They trust all channels of distribution but
have neutral attitudes toward supporting the local economy and the environmental
friendliness of OF products. Conversely, inexperienced OF consumers, or IOF, are consumers
who consume OF products on a regular base but do not trust any channel of distribution, as
they don't feel confident when buying OF products in those points of purchase. However,
they are principle-oriented consumers. It is important to note that that TOF have a higher
frequency of purchase than IOF.
TOF, SOF, and IOF consumers have been profiled using the socio-demographics data. Table
7 depicts all socio-demographic characteristics of the 3 segments. We used relative
measures, as there were missing values. It is readily seen that age, marital status, income,
place of residence, monthly spending, level of education, and occupation are determinant of
the OF clusters. However, results show also that gender is not a determinant of the
segments (Chi-square test is not conclusive: sig = 0.072 > 5%).

8.4.2 Motivations
Consumers were asked to rate their reasons to buy OF. Generally speaking, they value
health, taste, environmental friendliness, superior quality and the support of the local
economy. A one-way ANOVA was run to test differences between the three segments (TOF,
SOF, and IOF) with regards to the five reasons to buy OF. Overall, consumers in the three
defined segments have different reasons to buy OF (cf. Table 8). It is also clear that TOF
have the highest scores on all reasons to buy, with health, environment and local economy
being the most important ones. Conversely and as expected, IOF score the lowest on all
reasons to buy.

Reasons to Buy TOF SOF IOF Sig.

Health 4.72 4.35 3.95 0.000*

Taste 4.39 3.86 3.52 0.000*

Environment 4.69 4.35 3.94 0.000*

Quality 4.26 3.66 3.49 0.000*

Local economy 4.68 3.93 3.55 0.000*

Table 8. OF clusters and Reasons to Buy *sig. at 5%

8.4.3 Predicting consumers' membership
In order to assess the predictive power of each variable in predicting cluster memberships, a
discriminant function analysis was run. This analysis is used to determine which variable(s)
discriminate between two or more naturally occurring groups. For instance, after
segmenting the market using cluster analysis, managers would like to know (i) how to
classify new OF costumers according to a set of variables, and (ii) what variables allow the
best allocation and targeting of OF consumers. This is achieved through discriminant
analysis techniques. Results from Table 9 show that the frequency of OF purchases is the
best predictive variable as it correctly classifies 71.2% of the respondents. It is clear that
consumers in the three segments, i.e., TOF, IOF, and NOF, have a very complex
psychographic profile. This should lead companies to use an optimal mix of variables. In
our case, the combination of all variables provides 91.9% predictive power meaning that 9
consumers out of 10 are correctly classified if we consider a combination of the variables
listed in Table 9.