This study examines the behavior and attitudes of adult Generation Z cohort members in relation to entomophagy. Specifically, it explores their familiarity with insect consumption, prior experience, and willingness to consume certain insect-based foods and drinks. Lastly, the Z cohort is segmented based on their behavior and attitudes. Through online quantitative research, a valid sample of 742 questionnaires was collected. Data analysis included descriptive statistics, reliability analysis, factor, hierarchical cluster, and K-means cluster analysis, as well as chi-square tests. Results revealed that 41.4% are familiar with what insect consumption is, and no one had previously engaged in entomophagy. The insect-based food that the Z cohort is most willing to try is bakery products containing insect flour. The 88.5% of the Z cohort is not willing to replace meat protein with insect protein, and 20.4% are interested in obtaining more information about entomophagy. Moreover, 6.3% of the Z cohort is “willing” to participate in sensory tests, but when contact information was requested, only one factual name with phone number was provided. Segmentation of the Z cohort’s behavior was performed based on eight variables and four segments were identified: the “Future potential insect consumers” (29.1%), the “Rejecters” (26.7%), the “Disgusted, prefer to starve” (22.2%), and the “Inconsistent” (22.0%). Overall, the Z cohort is not food neophobic, but is unwilling to engage in entomophagy. Communication strategies are suggested to increase awareness and provide information about entomophagy and its benefits.

This paper explores the generation Z (Gen Z) cohort’s self-assessed knowledge regarding the coronavirus disease 2019 (COVID-19) symptoms as well as their interest in acquiring information and learning more about the transmission and spread of the severe acute respiratory syndrome coronavirus 2 (SARS-COV-2 virus) and the COVID-19 symptoms. Additionally, it investigates gender differences in self-assessed knowledge of COVID-19 symptoms. Field research employing a nonprobability sampling method with an online questionnaire resulted in collecting 762 valid questionnaires. Data analysis included descriptive statistics, factor and reliability analysis, and the independent sample t-test. Results reveal that overall symptom knowledge was assessed higher than the self-assessed knowledge of the 13 specific symptoms. No gender differences were detected regarding self-assessed knowledge of the following COVID-19 symptoms: cough, dyspnea, anorexia, productive cough with expectoration (phlegm), headache, and diarrhea. On the other hand, for self-assessed overall knowledge of COVID-19 symptoms, as well as self-assessed knowledge of COVID-19 symptoms related to fever and fatigue, myalgia (muscle pain), pharyngodynia, nausea–vomitus, hemoptysis, and abdominal pain, the t-tests conducted showed that there are statistical differences in knowledge assessment between male and female subjects. Based on the outcomes, the paper provides marketing communication practices targeting this young generation cohort to raise awareness so that Gen Z’ers may react effectively if these symptoms are observed and, thus, request medical assistance.

Instagram is perhaps the most rapidly gaining in popularity of photo and video sharing social networking applications. It has been widely adopted by both end-users and organizations, posting their personal experiences or expressing their opinion during significant events and periods of crises, such as the ongoing COVID-19 pandemic and the search for effective vaccine treatment. We identify the three major companies involved in vaccine research and extract their Instagram posts, after vaccination has started, as well as users’ reception using respective hashtags, constructing the datasets. Statistical differences regarding the companies are initially presented, on textual, as well as visual features, i.e., image classification by transfer learning. Appropriate preprocessing of English language posts and content analysis is subsequently performed, by automatically annotating the posts as one of four intent classes, thus facilitating the training of nine classifiers for a potential application capable of predicting user’s intent. By designing and carrying out a controlled experiment we validate that the resulted algorithms’ accuracy ranking is significant, identifying the two best performing algorithms; this is further improved by ensemble techniques. Finally, polarity analysis on users’ posts, leveraging a convolutional neural network, reveals a rather neutral to negative sentiment, with highly polarized user posts’ distributions.

The viticultural sector is facing a significant maturation phase, dealing with environmental challenges to reduce agrochemical application and energy consumption, while labor shortages are increasing throughout Europe and beyond. Autonomous collaborative robots are an emerging technology and an alternative to the scarcity of human labor in agriculture. Additionally, collaborative robots could provide sustainable solutions to the growing energy demand of the sector due to their skillful precision and continuous labor. This study presents an impact assessment regarding energy consumption and greenhouse gas emissions of collaborative robots in four Greek vineyards implementing a life cycle assessment approach. Eight scenarios were developed in order to assess the annual production of four Vitis vinifera L. cultivars, namely, Asyrtiko, Cabernet Sauvignon, Merlot, and Tempranillo, integrating data from two wineries for 3 consecutive years. For each conventional cultivation scenario, an alternative was developed, substituting conventional viticultural practices with collaborative robots. The results showed that collaborative robots’ scenarios could achieve a positive environmental and energy impact compared with conventional strategies. The major reason for lower impacts is fossil fuel consumption and the efficiency of the selected robots, though there are limitations regarding their functionality, lifetime, and production. The alternative scenarios have varying energy demand and environmental impact, potentially impacting agrochemical usage and requiring new policy adjustments, leading to increased complexity and potential controversy in farm management. In this context, this study shows the benefits of collaborative robots intended to replace conventional practices in a number of viticultural operations in order to cope with climate change impacts and excessive energy consumption.

Drama is a wine region in northern Greece with a local economy based on agricultural production. Despite its historical and geographical setting, the wine tourism industry is not very well developed in the wine-growing region of Drama. Although the sustainability of territorial development is often linked to local food and wine heritage, it seems that the implicated parties are not totally convinced. For this qualitative case study, we first conducted interviews with the management of the local wineries to determine the reason behind the low number of wine tourists in the area. Then, a workshop was organized, and all relevant parties were invited to increase awareness through education while also attempting to find mutually agreeable solutions through working group table discussions and the presentation of findings. Taking advantage of and preserving the cultural heritage through the cooperation of the related regional businesses, regional/central authorities, and non-governmental organizations (NGOs) was concluded to be an important tool for organizing new initiatives that will boost wine tourism.

In this work we expand on previous results, which were obtained by applying an exploratory factor analysis process. The analysis was carried out on a dataset constructed by means of a quantitative questionnaire regarding consumers’ degree of engagement with social media and their respective online decisions and actions. Thus, the model under study here integrates these three derived constructs; “engagement”, “decision”, and “action” as its building blocks. The aim of this work is twofold: to validate model’s fit leveraging a confirmatory factor analysis process and to investigate the relations between the three factors with structural equation modelling. With respect to the first objective, the measurement part of the model is verified, and its fit is tested and accepted under several heterogeneous indices. Secondly, the structural part of the model is validated against theoretical hypotheses regarding the relations between the three latent variables. Results show that both “engagement” and “decision” predict “action”, with the former however being more important. To the best of our knowledge, the specific model built around these three constructs is not found elsewhere in literature and can prove to be a valuable source of information for e.g., marketers in their effort to apply an efficient marketing strategy.