Measuring destination image and consumer choice criteria: the case of Mykonos Island

Destination image has long been identified as an environmental characteristic that influences consumer behaviour and choice. As destinations compete nowadays globally, marketers need to acquire new knowledge and a greater understanding of the business and the environment, in which they operate in order to determine and adopt an appropriate marketing mix. So, first research objective was to measure attitudes towards island of Mykonos in order to identify key dimensions and their relative importance in determining consumer choice. Then, Cluster analysis was performed in order to segment the market and identify different clusters of tourists. Four different clusters were identified based on choice criteria and attitudes. Results can be a valuable input for both marketers and practitioners

Young consumers' perception of food quality: an illustration from Greece

Food quality is becoming an increasing important factor in consumer's buying decision making, especially after serious safety issues appeared recently in the European Union. The need of standards to assure food quality and safety is evident from literature. The present study explores young consumers' perceptions of food quality. It presents the outcome of a field research undertaken from October 2002 to January 2003. A convenience sample of 582 higher education students aged between 18-23, living away from their homes, in Greece, was employed. Statistical analysis included frequencies, percentages, means, factor analysis, reliability and cluster analysis. The findings from this study are discussed, which are considered to be relevant to marketing practitioners and policy makers for designing appropriate marketing strategies in order to attract and satisfy these segments of young consumers. Lastly, suggestions for further research are presented

Knowledge assessment of covid-19 symptoms: Gender differences and communication routes for the generation z cohort

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. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

Mining textual and imagery instagram data during the COVID-19 pandemic

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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland