For Analysis 1, the “Sampling survey” methodology was used. The statistical population of residents in Sicily in 2022 was chosen as the starting point, which, according to the latest ISTAT census, is equal to N = 4,814,016 (ISTAT, 2022). This initial population was stratified by the following socio-economic characteristics: gender, age group, educational qualification, and annual family income. Some reflections were made aimed at obtaining a sample composition that was suitable for results not influenced by any socio-economic variable (characteristics of respondents). Therefore, for this study, we wanted a sample with a similar number of males and females, as well as in terms of income ranges. The average income in Italy is around €23,000–24,000 gross per annum for the general average of taxpayers (ISTAT, 2022), with significant differences between categories: employees around €23,000, self-employed workers around €64,600 gross (but with significant disparities), and pensioners above €19,700. Other ISTAT data for households (2023) indicate a median net household income of around €33,800 per year, but half of households earn less than €27,000, highlighting significant inequality. Considering that in Sicily the total number of employed persons (public plus private) was 1,181,000, of which 300,000 were public employees and 811,000 were private employees and self-employed workers (ISTAT, 2022), it was decided to divide residents according to income and to select for this study an equal number of respondents belonging to the two main income brackets, i.e., less than €25,000 and between €25,000 and €50,000.

Regarding age, a low priority was given to young people with a medium-high level of education, as they are likely to be more informed about blockchain and representative of future consumers.

The methodological choice to overrepresent young people and those with a high level of education in the final sample of 628 units deserves attention and should be discussed in more detail within the scope of the study, as it may overestimate the general interest in blockchain technology. Millennials (Generation Y) and Generation Z together represent approximately 75 to 80% of individual cryptocurrency holders in Italy, while Baby Boomers account for less than 1%. This group was oversampled in this study not to artificially inflate the perceived interest in blockchain technology, but because they represent the potential consumers of today and of the future.

Recent studies indicate that education level is one of the most significant factors in blockchain adoption (Castro and Au-Yong-Oliveira, 2021). Lack of awareness and skepticism about ease of use remain the main obstacles for those without an academic or technical background. More educated users tend to perceive the advantages of blockchain in terms of transparency and immutability, factors that foster trust in the system (Kasumu and Agbarakwe, 2025).

Low incomes are closely linked to low levels of education, both in Italy and globally. Istat data for 2025/2026 show that there is a strong link between low income and low levels of education (ISTAT, 2024). The most recent data from 2025 and early 2026 confirm that education remain the main driver of social mobility and earning potential. Among young adults (aged 25–34), the unemployment rate for those without a diploma is 12.9%, compared to 6.9% for those who have completed secondary or higher education (ISTAT, 2025).

A slightly higher number of young adults with a high level of education in the sample reflects a group of consumers who are more likely to understand the value of BT, but there is a risk of failing to grasp the real barriers (technical and psychological) that hold back the less educated general population. However, since the prices of these new high-quality products will certainly be higher than those of common products at the beginning of their commercialization, a slightly higher number of young adults with medium-high income and education levels seem appropriate.

As is well known, the sample size depends on the parameter to be estimated or the hypothesis to be verified, the type of sampling (block or repeated), the variability of the population, the acceptable error, and the probability assigned to the confidence interval. To decide on the sample size, the percentage of error that is acceptable must be established. The degree of confidence to be attributed to the result must also be established. Since in this case the population is very large, it is possible to consider valid the probability conditions of p = q = 0.5; therefore, a sample of n = 628 units was considered suitable for this study with the following conditions: p = q = 0.5, ε = 4%, and Prob = 95%. From the initial database obtained at the end of 2024 (database of 3,192 cases), the cases with errors or blank records (no answers) were excluded. Using R statistical software, it was possible to extract from the clean database of 3,146 cases (completed questionnaires) a sub-sample of n = 628 cases simultaneously (random bulk extraction), following the established rules: number of males = number of females, almost equal numbers for each age group with light priority for young people, average income of 37,000 euros, light priority for high education. The standard error (SE) measures the standard deviation of the sample distribution. With a sample of n = 620, the SE, which measures the accuracy of the sample estimate relative to the true value of the population, is equal to 1.99%. This methodology made it possible to draw inferences from the results obtained, and the stratification criteria applied increased the accuracy of the estimates.

Focus group (FG) methodology is a qualitative interview methodology often used for social research to collect qualitative information (Nyumba et al., 2018) and is based on literature for similar studies (Winter Falk et al., 1996; Osei et al., 2021; Altamore et al., 2018; Altamore et al., 2017). It is based on discussions among a group of people about a topic to be investigated in depth. The main objective of focus group methodology is to gather ideas, considerations, and suggestions from a group of individuals selected according to a well-defined target and invited to discuss freely and spontaneously on a specific research project (Hennink, 2007; Osei et al., 2021; Altamore et al., 2018; Altamore et al., 2017). It can be organized with consumers on a specific topic or product, or with experts (stakeholders or entrepreneurs) on issues of interest (Betts et al., 1996). Following the FG methodology, the discussion among participants is conducted by a moderator with experience in this type of methodology. The moderator must introduce a series of topics to the selected small group of individuals in an informal and seemingly unstructured manner and facilitate discussion without influencing the opinions and relationships between participants, ensuring that the flow of information proceeds toward the objective of the discussion (Krueger, 1998; Hennink, 2007; Altamore et al., 2018; Altamore et al., 2017). The selected group of participants must consist of a minimum of 8 and a maximum of 13 people, who can be homogeneous or not in terms of social and economic characteristics depending on the aim of the study (Betts et al., 1996). The maximum duration of the session must not exceed 90 min (Betts et al., 1996), and in this study, the session lasted 60 min. The discussions were carried out at the research center of the Co. Re. R. A. S. (Consorzio Regionale per la Ricerca Applicata e la Sperimentazione, one of the main partners of the SFINGE project, see Introduction). Before each session began, participants were asked if they would like to take part in this study. If they answered affirmatively, they were asked to give their consent by signing a written consent form.

The participants’ discussion was recorded by the authors and subsequently transcribed verbatim (Betts et al., 1996; Altamore et al., 2018; Altamore et al., 2017). The information obtained was analyzed in accordance with the methodology for analyzing these types of results (Hennink, 2007). This study involved the full transcription of audio/video recordings and the development of a shared opinion hypothesis among participants, identifying the relationship between discussions to explain the phenomenon under study. In addition, the content of the conversation was analyzed using NVivo v. 15 software (R Time Scientific Solutions) to quantify the frequency of certain terms or themes (where appropriate) and to describe opinions qualitatively. NVivo is qualitative data analysis software used to organize, analyze, and find logical patterns in large volumes of unstructured information. It is widely used in social sciences, marketing, and business intelligence (Ingrassia et al., 2022a). It is also particularly useful for managing and analyzing data that are not strictly numerical, such as texts, web and social media content, multimedia and files.

Vos Viewer software was also used for Analysis 3 “Focus Groups with Experts” to process the information obtained from the verbatim transcription of the focused group discussion (Chironi et al., 2020) and to obtain the co-occurrence Scientific Map (van Eck and Waltman, 2010; Kirby, 2023; Chinnici et al., 2025), which is a graphical representation that may highlight elements that do not emerge clearly from vast discussions and discover interconnections about the topics discussed that can be linked in a network (van Eck and Waltman, 2010; Bellia et al., 2022). In this case, it was used to create a conceptual map of links among the main topics discussed and highlight the main ideas on which to build the marketing model (in particular the Vision and the Mission).

For Analysis 2—“Focus Groups with consumers” (AN2), two sessions, each with eight different consumers, were carried out. When choosing the group, it is necessary to consider the variables that influence the purchasing choice and the product’s consumption. On this basis, a heterogeneous sample of individuals, as described in the sampling methodology (Par. 3.1.), was chosen (Trost, 1986). Therefore, the participants were selected according to different consumer profiles previously identified by the research group based on predefined segmentation criteria from marketing literature (Keller and Kotler, 2009; Kotler et al., 2019; Peter et al., 2024) and previous works (Altamore et al., 2018). The four identified profiles differ in terms of age range and other socio-demographic characteristics, consumption behaviors, and lifestyles (Table 1).

For Analysis 3—“Focus Groups with experts” (AN3), three sessions in two different times (six sessions in total) were carried out with the partners of the SFINGE project, i.e., farms, and other enterprises (wheat producers, mills, pasta factories), actively participating in trials, in the implementation of the integrated traceability system for cereals and pseudocereals in Sicily, and in the creation of experimental fields; the company involved in the configuration, adaptation and segregation of the centralized blockchain system for the traceability of personal and geographical information of participating companies; wheat producers, mills, pasta factories, industrial bakery manufacturers, who are willing to implement the blockchain certification (from seed to flour at the regional level) and other qualified persons like, including the scientific managers of the research bodies involved in various capacities in the project, the managers of the Department of Agriculture responsible for the Rural Development Program measure through which the project was funded, and a number of university researchers who are experts in marketing and ancient Sicilian grains from an agronomic perspective.

These sessions of focused groups’ discussions were carried out to discover vision, mission, strengths and weaknesses, opportunities and threats of experts in this sector (Objective 2).

In total 24 people participated in the sessions with experts (divided as eight people for each focused group). In particular, the first three sessions were organized in 2022, after one year of project, and the second three in 2024, at the end of the SFINGE project (after having implemented the blockchain certification from seed to flour at the regional level).

For the design of a marketing and communication model/strategy for branding, positioning, commercializing, and communicating the value of “new products” (i.e., SAG products with BT certification), results from Analyses 1, 2, and 3 were used as inputs. SWOT Analysis, PEST Analysis, market analysis, analysis of competitors, and analysis of the dimensions of communication by the AGIL methodology were carried out (Mancini et al., 2019; Schimmenti et al., 2021).

For identifying the communication dimensions of these ‘new products’, the AGIL model developed by Parsons (1965) was used, which includes the four ‘Dimensions of Communication’ (Parsons, 1965). Multidimensional methods are used for complex phenomena so that they can be broken down into simpler components and are generally applied to analyze communication (Ingrassia et al., 2018; Ingrassia et al., 2022b; Ingrassia et al., 2024). The basic assumption of the paradigm from which Parsons starts, in his functionalist interpretation of social systems, is that every system, to survive and develop, must be able to solve four classes of functional problems referred to by the four letters of the acronym: A = Adaptation (adaptive function); G = Goal attainment (achievement of goals); I = Integration (integrative function); L = Latent pattern maintenance (maintenance of the latent model). The authors’ elaboration of Parsons’ model developed for this study is shown graphically in Figure 2 (Ingrassia et al., 2018; Ingrassia et al., 2022b; Ingrassia et al., 2024). These four dimensions are displayed in quadrants on a Cartesian axis and must be read counterclockwise, starting from the upper right quadrant of the adaptation dimension, which represents maximum “symbolic complexity” and maximum “contingency of action,” and ending with the latent patterns dimension, which represents minimum “symbolic complexity” and minimum “contingency of action” (Ingrassia et al., 2018; Ingrassia et al., 2022b; Ingrassia et al., 2024). The first quadrant is the dimension of adaptation (A), which refers to a key aspect of communication that involves the efficient use of resources and methods in the service of a compelling purpose. The goal is to adapt communication approaches to different social structures, such as the market, mass media, digital media, and institutions. The second quadrant is the dimension of goal attainment (G), a cognitive aspect of communication that operates based on the information distribution function and refers to the principle of achieving the desired goals of information dissemination. The third quadrant is the integrative function (I), which emphasizes the norms and values of the community in which communication takes place, as well as the principles and traditions shared by the community. The fourth quadrant is the latent patterns dimension (L), linked to communication as a means of expressing one’s identity, whether individual or collective; it should measure consistency with shared values and ideas.

Based on the methodology for AGIL analyses, the participants in all focus groups (both consumers and experts) were asked to rank the importance and appropriateness of the selected indicators identified using a 5-point importance scale: 1 - not important/completely inappropriate, 2 - slightly important/inappropriate, 3 - moderately important/appropriate, 4 - very important/appropriate, 5 - absolutely important/very appropriate. The indicators identified are social networks, TikTok, Instagram, X (formerly Twitter), mobile messaging apps, the Internet, school, academic degree, post-graduate degree, family and friends, work colleagues, other institutional sources, newspapers and magazines, radio, digital disclosure platforms, YouTube, PayTV, and other (Figure 1).

The effectiveness of each dimension was calculated as the sum of scores assigned to each dimension divided by the sum of scores assigned to all dimensions (Ingrassia et al., 2024).

% DimEffect = ( Sum of score assigned to one dimension / Sum of scores assigned to all dimensions ) x 100

Concerning the socio-demographic characteristics of the sample, including city of residence, gender, age, level and field of education, occupation, family composition, and average annual income, results are shown in Table 2.

Most of the respondents, 91.7%, are from Sicily, and the remaining people are residents in Sicily but from other regions or countries. Results regarding gender show that 50% of the total are male, 48.3% are female, and 1.7% of respondents did not specify.

Regarding the age of respondents, the highest percentage, equal to 36.7%, falls within the 27–39 age group, followed by the 40–49 age group (17.5%). The remaining respondents are equally distributed across the other age groups, except for those over 70 years old (2.5%). About educational qualifications, 48.3% stated that they had a first and/or second level university degree, while 33.3% had a high school diploma or less, and 16.8% had a master’s degree/PhD or other qualification. Specifically, when asked to elaborate on the subject area of their studies, 44.2% of respondents said they had studied natural/earth/environmental/agricultural sciences, 20.8% economics/law, and 12.5% for both humanities/literature and mathematics/computer science. Lower percentages were recorded for other types of study. With regard to occupation (Table 2), 42.5% of the sample said they were employees, both in the public and private sectors, 15.0% were teachers/professors/researchers, 13.3% were entrepreneurs or freelancers, and 10.8% were students. A smaller percentage of respondents were employed in other occupations, while 15.0% were unemployed. The household consisted of three or four people in 57.5% of cases, two persons in 25.8% of cases, 10.8% lived alone, and 5.9% lived with more than four people. When asked about their average annual income, 44.5% said they had an average annual income of less than €25,000; 40.2% had an average annual income of between €25,000 and €50,000, while 15.3% said they earned more than €50,000 per year.

It was asked about consumers’ information regarding SAG (Table 3). Almost all respondents said they had heard of ancient Sicilian cereals (91.1%). It is interesting to note that when asked what ancient grains are, respondents expressed conflicting opinions between the two answer options, giving greater prevalence to ancient varieties that enjoy qualitative characteristics such as greater nutrition and digestibility. Furthermore, as these products are strongly linked to the territory and its traditions, it can be noted that the main source of information on these grains comes from places of consumption such as restaurants/pizzerias (20%), although the Internet and social networks together account for 21% of the total.

Regarding respondents’ knowledge of the characteristics of products made with SAG (Table 3), results show that consumers have no correct knowledge about them. In fact, 68.2% of the respondents claim that they help against digestive disorders, and 74.7% claim they have superior health benefits compared to other types of grain. However, 89.2% of respondents say they purchase and consume SAG-based products.

Table 4 shows consumers’ responses about the consumption and purchase of products made with SAG, with details of different age ranges and educational levels. Results show that no group of consumers declares they consume and purchase foods made with Sicilian ancient grains.

In general, all respondents are primarily interested in both consuming SAG outside the home and purchasing them for home consumption (56.5%), while 33.4% consume them only outside the home. The youngest consumers, ranging from 16 to 39 years old, are the most interested in both consuming outside the home and purchasing SAG products for home consumption. Among these, respondents with a degree (any level) show the greatest interest (25.6% of the total). These results also reflect the characteristics of the sample, which is composed of a slightly higher number of young people aged 16 to 39 (51.7%, Table 1). In general, the respondents most reluctant to consume this type of product (at home and outside the home) are those aged 60 and over, who are less inclined to try new consumption experiences and change their consumption habits.

Regarding preferences for pasta, bread, or pizza made with grains grown in Italy or Sicily, and preferences for SAG or quality certifications, Table 5 shows the results of the respondents’ preferences for products with grains grown in Italy, Sicily, and for SAG. Results highlight that consumers prefer products made with domestic wheat; for 43.6%, Italian wheat is extremely preferred, while for 56.7%, Sicilian wheat is extremely preferred. Regarding SAG, consumers show high appreciation for pasta, pizza, and bread, with a percentage of “Extremely preferred (10-9)” higher than 30%. About products with quality certifications such as Protected Denomination of Origin (PDO), Protected Geographical Indication (PGI), or organic certification (Ingrassia et al., 2022a; Altamore et al., 2017; Altamore et al., 2018), respondents show a positive preference (70% preference in the 5–10 range). On the other hand, they are very reluctant to consume these types of products enriched with functional components (Table 5).

In accordance with the literature (Altamore et al., 2018), the sensory appreciation of SAG by consumers is a very important finding because taste, smell, and visual appearance are some of the primary drivers of liking for food products. Consumers declared that the different tastes and aromas are noted mainly for pizza, and improved digestibility was noted for pasta and bread (data not shown). Results also highlight the misconception that these products are easier to digest than others made from modern hard grains (data not shown).

About consumers’ knowledge of blockchain-certified SAG-based products, it emerged that as many as 70.7% of respondents were unaware of the existence of this type of certification for food products. Respondents were then asked, in the case of purchasing pasta, bread, and pizza made with SAG and certified with BT, what attributes would be most important to them when choosing these foods (Figure 3). The set consists of the following options: produced with organically grown grains, different taste and flavor compared to traditional pasta, better digestibility of these products, and presence of a new logo on the packaging. In general, all characteristics received the highest preferences (7–8 and 9–10) for all products, showing very good interest from respondents, but particularly high ratings were given to the improved digestibility characteristic and different taste/flavor (Figure 2).

Additionally, respondents were asked if they would prefer a public or private entity for SAG-based blockchain-certified products and which specific entity they would desire as a guarantor of the authenticity of the product and supply chain traceability to assure compliance with the rules of blockchain in the supply chain and to monitor the blockchain functioning (Table 6). Almost half of the sample believes that this role should be carried out by a public authority linked to agricultural issues, such as the Italian Ministry of Agriculture and Food Sovereignty (25.8%) or the Regional Department (23.3%). Some respondents (24.2%) think that a regional research consortium specialized in cereals could be the controller of blockchain certification for foods produced with SAG, and 14.2% believe that this role should be assigned to a private organization (Table 6). The most referred channel for communicating information about blockchain-certified SAG-based products is publicity through mass media (TV, radio, newspapers, and online) planned by public institutions.

Figure 3 shows the consumers’ responses related to the best way of communication to convey information regarding food certified with blockchain technology, categorized by age group. Among the youngest respondents (16–26), the most preferred means of information is public institutions’ social media (8.5%), followed by advertising by public institutions (region/ministry) through traditional media, such as TV and radio (6.7% of respondents). It is interesting to note that, in the 16–26 age group, respondents expressed no preference regarding information from schools, possibly because they have already received it. Similarly, the highest preference among people aged 27–39 is for traditional advertising by institutions, with 15% of respondents. In this age group, 5.5% of respondents claim that information from schools could be a good means of communication. In general, for the older age group (60 years old and above), institutional advertising was observed as the best way of communication, despite a lower percentage.

The final part of the survey focused on the willingness of respondents to pay a higher percentage of the price for blockchain-certified SAG-based products compared to the actual price of common pasta, bread, and pizza (Table 7). The result is almost the same for both products, and the most significant finding is that more than 30% of respondents would pay from 5 to 15% more than the price of conventional products. On average, among the three products, 27.8% of respondents are not willing to pay more than 5%. Although referring to only a few respondents, the number of those who would pay 30% more for blockchain-certified pasta is higher than for the other products (6.7%).

These findings are in line with previous literature on consumers’ WTP for pasta certified with blockchain technology (Petrontino et al., 2024).

Consumers’ willingness to pay was analyzed according to different income ranges (Figure 4). As expected, the results show that in the lowest income range, most respondents are willing to pay a higher price of up to 5% (16.6%); 11% are willing to spend even up to 15% more. In the middle-income range, between €25,000 and €50,000, most respondents (17.7% of the total sample) would be willing to pay 5–15% more than the average market price for the same products made from common wheat. In the income range above 50,000 euros, the highest percentage of respondents (6.3% of the total sample) would be willing to pay 15 to 30% more. For each income range, a low percentage of respondents appear willing to spend a markup above 30% of the standard price to buy food products made with SAG certified with BT.

In conclusion, the results of Analysis 1 revealed the following considerations:

This part of the research aimed to supplement the results of the questionnaires with qualitative information gained through focus groups with consumers (Osei et al., 2021). It investigated consumers’ knowledge about SAG and blockchain certification for food supply chains. The products made from Sicilian ancient grains that consumers were asked to discuss regarding the possibility of blockchain certification included pasta, bread, and pizza. Moreover, participants were asked about different means to access the required information, such as QR codes.

Table 8 shows the questions asked to participants during the sessions and the frequencies of their answers. The results indicate that the group discussions led to a single shared perspective among individuals. Consumers discussed the quality of the food products they usually consume, the related label characteristics (such as the possibility of having additional information on them), and the link between food products and sustainability. The results show that only 62.5% of respondents are aware of the quality of the food they consume. Only 46.8% regularly read product labels, and 31.3% sometimes do so. The most sought-after information by consumers on labels was the expiry date (30.7%), followed by nutritional information (14.7%). Regarding the additional information that consumers would like on the label, “attention to consumer health” was prioritized (30.7%), followed by “attention to the environment” and “production method” (20.9%).

About the willingness to change their eating habits for sustainable behaviors, respondents are inclined to change their habits, with 65.6% of the total; 31.3% are reluctant to change. However, consumers are willing to spend more money to buy food that contributes to environmental sustainability, responding positively to the possibility of paying a price markup of between 5 and 20% more. They have stated that they are willing to do so if this represents their contribution to human health and environmental sustainability. This is in line with social responsibility theory, which argues that individuals and organizations may want to act in ways that benefit society and the environment, not just themselves. It is an ethical framework that suggests consumer behavior can be influenced by awareness of its impact on the community at large and the environment and that individuals should strive to maintain a balance between economic growth, social well-being, and environmental sustainability. In practice, decisions and actions should be guided by moral values and a sense of what is right and wrong for the collective good.

Moreover, the results of Analysis 2 revealed that focus groups have little knowledge of blockchain for the agro-food sector, but they know food products with quality certifications, such as Protected Denomination of Origin (PDO), Protected Geographical Indication (PGI), DOC (Italian PDO for wines), or organic (Ingrassia et al., 2022a). They also requested information on the percentages of SAG used in the preparation of products (like pasta, pizza, and bread) and tables to help them know the quantities of SAG contained. The information desired in the QR Code covers both the companies involved in the preparation of the product (from the farms to the final processor or distributor) and the technologies used in the various stages of processing, thus making the supply chain traceable and transparent. Furthermore, participants highlighted their desire to access information about BT and SAG, asking that it be provided in a clear and easy-to-understand manner to understand what blockchain certification is, what the advantages and added value are for consumers, the costs and commitments required by businesses, and the valuable characteristics of SAG. The opportunity emerged to use channels accessible to all, such as schools, social networks, and stands in high-traffic areas where it is possible to interact with communicators. In addition, there is a need to experience these products in public places such as school and university canteens or hospitals, to enhance credibility and awareness through consumer experience.

The key elements that emerged from Analysis 2 are the following:

This part of the research aims to gain in-depth information through focus groups with experts. During discussions, the experts expressed their opinions regarding the motivation for achieving certification with BT, investigating their knowledge about this technology and the competence needed to apply it. Moreover, the ideal advantages of BT for the certification of the SAG supply were discussed. Participants also discussed the funds and resources needed for implementing the requirements to achieve this certification, as well as the possible target market and marketing/communication strategies. The main reason for adopting BT for the SAG supply chain lies in the desire to give these wheat varieties a quantifiable and demonstrable added value, giving producers greater bargaining power against wholesale intermediaries. As for marketing, participants discussed suitable strategies for positioning the “new products” in the market. In this context, the price was identified as one of the most important marketing levers. In fact, the experts claimed that to commercialize such ‘new products’ of high quality—which require higher production costs—and to communicate this quality (in a way that is perceived, understood, and accepted by consumers), a price differentiation should be made, with slightly higher prices than those of similar products without blockchain certification. The higher price positioning stems from several factors intrinsic to this type of market, such as specialization, higher perceived quality, and higher production costs for smaller volumes. However, according to participants, the final pricing strategy should depend not only on the specific market structure but also on the decisions of entrepreneurs in the supply chain who would like to market these products at relatively affordable prices for consumers (locally in the first instance). The presence of one or more mills and pasta factories dedicated to serving a cooperative producer might give producers greater negotiating power, as they would no longer have to depend exclusively on wholesalers, intermediaries, mill owners, and pasta factories to set the selling price of wheat. This would mitigate the costs of production and implementation of the BT for farmers, allowing for greater profits.

A new brand name and a new logo for these ‘new products’ with blockchain certification appeared crucial to univocally recognize these products in the market and to design a specific marketing strategy. This would help consumers recognize and differentiate these products from common ones. The added value of these ‘new products’ therefore derives not only from the high value of unique raw materials and socio-economic characteristics but also from the certification with BT for authenticity of products, food safety, and traceability of production, processing, and logistics.

Regarding the topic of communication, one important factor that emerged was the role of public institutions in communicating appropriate information to consumers and stakeholders and in educating consumers about sustainable food consumption.

VOS Viewer (van Eck and Waltman, 2010) software was used to visualize the interconnections between the main topics discussed by experts. This software simplified the authors’ interpretation of all the issues addressed and highlighted the latent connections that are difficult to grasp from simply reading the transcripts of the six focus group discussions. This software analyzes the text by creating a map of interconnections that highlights the co-occurrences of words within a text/discussion (Kirby, 2023). The co-occurrence map generated by the software allowed us to see how the various words are linked to each other through many connections. The larger spheres, whose size indicates the number of co-occurrences and connections with other topics, represent the words that were mentioned very frequently during the discussion, thus reflecting the main topics discussed by the participants. The words in the center of the map are the crucial ones to consider for the design of the marketing model. The starting point for analyzing the content of the discussion consisted of a faithful transcription (verbatim methodology), ensuring that the natural language used by the participants was preserved in its entirety. This methodological choice was essential for preserving the semantic richness and expressive nuances of the qualitative data. Subsequently, the raw text corpus underwent a cleaning or normalization phase, necessary to optimize the input for VOS Viewer-assisted analysis. Specifically, extra-linguistic components (e.g., “um,” ‘ah, “laughter”) and annotations not relevant to the discourse content were removed, followed by punctuation formatting and finally the removal of high-frequency functional words (articles, prepositions, conjunctions, etc.), whose exclusion does not alter the semantic content but improves the effectiveness of the lexical analysis. After obtaining the normalized text corpus, it was then imported into the VOS Viewer software to visually map the relationships and thematic clusters emerging from the participants’ discourse. As a first step, after entering the data into the software, it emerged that, with a minimum number of occurrences of a term equal to 5, of the 1,053 terms present in the text, 114 met the threshold. Based on this score, the most relevant terms were selected. The default setting of the software is to select the 60% most relevant terms, which led to the selection of 68 terms.

The conceptual network in Figure 5, proved to be dense, and the terms with the highest centrality and frequency were “company,” “product,” “supply chain,” and “system.” These terms represent the pillars around which all the participants’ discussions developed, and in fact, the map confirms that the debate focused on the complex relationship between the need for management innovation (Red Cluster), the market structure and logistics (Green Cluster), and the central role of the wheat producer and the product (Yellow Cluster), all supported by information systems and projects (Blue Cluster).

In the second stage of analysis, further screening was carried out to focus the analysis on the most frequent and central themes. Of the 1,053 terms in the text, with 9 as the number of occurrences of a term, 53 met the threshold. Based on this score, the most relevant terms were selected. The default setting of the software is to select the 60% most relevant terms, which led to the selection of 32 terms.

The removal of low-frequency terms produced a more compact and interpretable network (Figure 6), and the previously identified central nodes (“company” and “supply chain”) were reconfigured. The conceptual network shows that the primary interest of the participants is not only technological innovation (blockchain) but its targeted application (Project) to a specific value product such as Ancient Grain (Yellow Cluster), whose success depends on the integration of strategic vision (Red Cluster), operational efficiency (Green Cluster), and a solid collaborative environment (Blue Cluster).

Finally, in the last phase of the analysis, a further selection was made. With 20 as the number of occurrences of a term, of the 1,053 terms present in the text, 16 met the threshold. Based on this score, the most relevant terms were selected. The default setting of the software is to select the 60% most relevant terms, which led to the selection of 10 terms. Figure 7 illustrates the logical path of the debate and shows a focused discussion that develops sequentially from the starting point (Blue Cluster), with the concepts of “ancient grain,” “supply chain,” and “traceability,” identifying how to trace and guarantee the origin of the raw material, the turning point (Green Cluster) where the term “blockchain” acts as a pivotal point and technological solution that responds to the need for ‘traceability’ and connects the “supply chain” directly to the “consumer,” creating a mechanism of trust, and finally (Orange/Red Cluster), culminating in the practical and regulatory aspects, represented by “logistics,” “market,” “certification,” and “code of ethics”.

The most important and recurring words during the discussion were the following: blockchain, supply chain, certification linked thanks to traceability and ancient grain. It is possible to see three main clusters: the green cluster (blockchain, consumer, partnership), the blue cluster (supply chain, traceability, ancient grain), and the red cluster (certification; market, logistics, and ethical code). The green cluster represents what entrepreneurs want to achieve with blockchain. The blue cluster expresses their desire to attribute value to ancient grains and is directly linked to the green cluster via the word blockchain. Finally, the red cluster highlights how entrepreneurs want to present themselves to consumers in the market through certification, a code of ethics, and smart logistics.

The main attributes considered fundamental by the expert entrepreneurs are ultimately connected with two interesting findings: partnership and ethical code. Partnership means that the experts ask for strong “cooperation” among all the interested parties, showing the will of entrepreneurs to base their choices on a common vision, the ethical code, and the importance they attribute to blockchain for the certification of the entire supply chain. The ethical code appears to be a very interesting finding, as it represents a declared commitment to social responsibility toward consumers and the environment (but also toward producers, who are the weakest link in the supply chain of ancient Sicilian grains).

To summarize, the main takeaways from Analysis 3 are given below:

The proposed model of strategy comes from the authors’ elaboration of results from Analyses 1, 2, and 3 and on solid literature on marketing systems (Kotler and Keller, 2015; Kotler et al., 2019). Thanks to these findings, a suitable marketing model for the supply chain was designed. The main aim of the model was to exploit blockchain certification as an added value to be communicated for these products to consumers and stakeholders. Moreover, another important element to be considered in this model is the cooperative role of all the entrepreneurs in the supply chain, since competition among actors in the supply chain would have damaged all entrepreneurs, especially farmers. The marketing strategy identified is the implementation of an integrated, multi-channel, cooperative (non-competitive) marketing system (Kotler and Keller, 2015; Kotler et al., 2019), as it was considered the best solution to create value for products made with certified SAG using BT in the various phases of the marketing strategy (positioning, marketing, and communication). This marketing system, in this case, is one in which the players in the supply chain cooperate rather than compete (Kotler and Keller, 2015; Kotler et al., 2019). This model has the advantage of allowing entrepreneurs to meet the maximum possible demand (a significant share of the market, if not, at least initially, all local demand) and reducing mark-up disparities between the different levels of the supply chain. This could allow for a fairer distribution of profit margins, to the benefit of grain producers, who are generally the weakest link in the chain. It envisages the use of multiple commercialization channels (pasta factories, bakeries, restaurants, pizzerias, retail stores, large-scale distribution, etc.), and so it seems the most suitable in this case. In fact, through the integration of these channels, it will be possible to increase the target market share, have lower costs of access to certain channels, and make more customized sales for specific target groups. Moreover, results of questionnaires and focus groups with consumers and experts revealed that ‘food safety’ or ‘traceability’ and ‘sustainability’ are the key elements for both entrepreneurs and consumers.

Therefore, the shared elements through which entrepreneurs satisfy consumer needs are given below:

It is interesting to note that both groups believe that these new products should not be sold at too high prices but rather at prices affordable to average incomes. In addition, both entrepreneurs and consumers feel a responsibility to the environment and future generations and therefore share ethical values that are represented by these products. Ultimately, both consumers and entrepreneurs agreed on some salient elements characterizing these new products with a certified supply chain, namely that a public authority to ensure safety for consumers and entrepreneurs should supervise blockchain certification.

The value communication theory assumes that companies can influence specific target groups by choosing the right messages and channels to use (Kotler and Keller, 2015; Kotler et al., 2019). This theory argues that companies can influence target groups by selecting messages and communication channels that reflect their values. By understanding the needs, motivations, and communication preferences of a target group, companies can tailor their messages to make them more persuasive and impactful. After segmenting consumers, the target audience is identified based on socio-demographic, psychographic, and behavioral data, and the most appropriate channels and messages are selected to reach them. Following this theory, once the needs of consumers can be satisfied by the ‘new products’, it will be necessary to build brand awareness and then brand attitude, i.e., help consumers evaluate the perceived ability of the brand to satisfy current needs (evaluate how consumers perceive the brand’s ability to satisfy their needs). Finally, it will be necessary to influence the purchase intentions of ‘new products’ through advertising activities, promotional offers, tasting stands with free gifts, etc.