**3. Results**

#### *3.1. Sample Description*

The sample was composed of 852 respondents aged between 18 and 90 years old, with an average of 37 years and six months. Sixty-five percent of respondents were female. Table 2 reports the social and demographic characteristics of the sample.


**Table 2.** Sample demographics characteristics.

Table 3 illustrates the distribution of the variables. The majority of respondents stated that they buy organic food at least "seldom". Eighty-seven percent of respondents stated that product origin was an important or very important driver of food purchase decisions (Table 3). Such a high figure is explained by the broad meaning of the term origin that in the consumers' mind, is associated with local food, Italian food, typical products, and, in a broader sense, even to food safety. The value origin and the specific reputation for very local products are extensively documented in the literature [11]. Region of origin evokes tradition, habits, culture, and so on, and these aspects directly influence preference for a regional product [56–58].

**Table 3.** Distribution of selected variables.


Note: BUYBIO: Do you buy organic food? ORIGIN: How important is product origin when deciding your food purchase? NNT: There is no need for new food technologies because there are so many types of foods; NTQR: The benefits associated with innovative food technologies are often overestimated; NTQL: New food technologies reduce the natural quality of foods.

Table 3 reports the distribution of the three original variables (NNNT, NTOR, and NTLQ), and Figure 1 illustrates the distribution of TFI.

Our respondents reported different willingness to try food with upcycled ingredients of olive leaves based on environmental and nutritional concerns. Our finding is that products with different attributes (nutritional or environmental) can be appealing to different consumers. Figure 2 illustrates the distribution of the two variables.

Variables TNUT and TENT are not independent. A Fisher's exact test rejected the null hypothesis of independence at a 99% confidence level. Table 4 reports Pearson's standardized residuals from the contingency table, showing that the diagonal elements were positive, while the off-diagonal ones were mostly negative. (Pearson's standardized residuals are computed by subtracting the expected frequency in a given cell under the null hypothesis of independence from the actual observed frequency and then dividing by the square root of the expected frequency.) This result suggests a positive association and that the two drivers of consumer acceptance do not offset each other on average. The result is of particular importance because it shows that the two drivers may be pursued at the same time.

**Figure 1.** Distribution of the Technophobia Index (TFI).

**Figure 2.** Willingness to try food containing upcycled ingredients with superior nutrition properties (TNUT) or lower environmental impact (TENV).

**Table 4.** Pearson's standardized residuals from a two-way table of superior nutrition properties (TNUT) and lower environmental impact (TENV).

