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Apples have always been considered a healthy product able to provide curative properties to consumers. In Italy, there is a long tradition of apple consumption and production both as a fresh product and as processed food. However, as with many other products, the consumption of fruits and vegetables and, more specifically apples, has been drastically affected by the first lockdown in 2020. In this project, the authors investigate whether the change in consumption habits had long-lasting consequences beyond 2020 and what are the main eating motivations, food-related behavior and socio-demographic affecting the consumption of fruits and vegetables after the pandemic.

The authors ran two online surveys with 1,000 Italian consumers across a year (from October 2021 to December 2022). In the study, participants answered questions about their consumption habits and their eating motives. Out of 1,000 consumers, the authors included in the final analysis only the participants who answered both surveys, leaving a final sample of 651 consumers.

The results show that participants have allocated more budget to fruit and vegetables after the lockdown than before it. Moreover, consumers reported an average increase in the consumption of apples. However, the increase was more pronounced for people aged between 30 and 50 years old and identified as female. After showing the difference across time, a cluster analysis identified three main segments that differ in their eating motives, place of purchase and area of residence.

Overall, the results contribute to a better understanding of how the global pandemic is still affecting people's daily life. Moreover, the findings can be used to guide the marketing and communication strategies of companies in the food sector.

To the best of the authors' knowledge, this is the first study that investigates changes in the consumption of fruits and vegetables, and, more specifically, apples, in Italy more than one year after the beginning of the COVID-19 pandemic. Moreover, the study proposes a classification of consumers based on their habits in a time frame during which the COVID-19 wave was at its bottom which is not currently present in the literature.

The goal of this study is to understand if ethnocentrism exists at the sub-regional level among honey consumers living in the same production area as a protected designation of origin (PDO). Moreover, this analysis explores if ethnocentrism is influenced by individual economic conditions, among other socio-demographic characteristics.

A sample of 725 consumers was collected through the use of a questionnaire that was circulated in the province of Varese, one of the few honey PDO areas in Italy. The authors performed a principal component analysis and a two-step cluster analysis to identify different PDO honey consumer segments, focusing on their interest for PDO attributes.

The authors identified four consumer segments, depending on socio-demographic, consumption habits, frequencies, preferred attributes and preferences for the PDO product. One cluster exhibited strong preferences for the PDO honey, in the spirit of ethnocentrism, and was characterised by low-income levels; ethnocentric preferences were also observed in another cluster that had a different socio-economic profile.

Honey is a niche product and not universally diffused among consumers: further analyses should investigate sub-national ethnocentrism for more universal food products. Yet, through the inspection of the different profiles found, it was possible to devise marketing strategies to boost PDO honey purchasing and to bring consumers closer to PDO products.

This analysis considers ethnocentrism as a segmentation criterion for PDO honey consumers that live in the very same PDO honey production area and enriches the existing literature on the relationship between ethnocentrism and individual economic status.

Digital markets are increasingly constructed by an interplay between (non)human market actors, i.e. through algorithms, but, simultaneously, fragmented through platformization. This study aims to explore how interactional dynamics between (non)human market actors co-codify markets through expressive and networked content across social media platforms.

This study applies digital methods as cross-platform analysis to analyze two data sets retrieved from YouTube and Instagram using the keywords “sustainable fashion” and #sustainablefashion, respectively.

The study shows how interactional dynamics between (non)human market actors, co-codify markets across two social media platforms, i.e. YouTube and Instagram. The authors introduce the notion of sticky market webs of connection, illustrating how these dynamics foster cross-platform market codification through relations of exteriority.

Research implications highlight the necessity to account for all involved entities, including digital infrastructure in digital markets and the methodological potential of cross-platform analyses.

Practical implications highlight considerations managers should take into account when designing market communication for digital markets composed of (non)human market actors.

Social implications highlight the possible effects of (non)human market co-codification on markets and consumer culture, and corresponding countermeasures.

This study contributes to an increased understanding of digital market dynamics by illuminating interdependent market co-codification dynamics between (non)human market actors, and how these dynamics (de)territorialize digital market assemblages through relations of exteriority across platforms.

This study develops a modified food-related lifestyle (FRL) instrument to analyse Kosovo consumers' fruit consumption behaviour and attitudes.

The research study is based on a structured questionnaire designed using a reduced version of the FRL instrument, including evaluation factors related to fruit consumption, which is useful to describe a fruit-related lifestyle. Data were collected through a face-to-face survey with 300 consumers in three main cities in Kosovo. A principal component analysis (PCA) with Varimax rotation and Kaiser Normalisation was performed to interpret and investigate fruit-related lifestyles. Cluster analysis was performed to analyse market segments, using the identified factors obtained from the PCA, a hierarchical clustering algorithm with a Ward linkage method and the K-means clustering technique.

Consumption behaviour is motivated by health concerns (perceived), fruit (nutrition) content and consumption habits. Four distinct consumer clusters were identified based on the fruit-related lifestyle instrument and analysed considering the different fruit purchase and consumption behaviour, attitudes towards health, quality, taste and safety.

The authors adapted a survey tool based on a reduced FRL instrument to elaborate a specific survey instrument suitable to describe the fruit-related consumer's lifestyles. The instrument was not designed according to the standard scales design procedure, but it is a first step towards creating a fruit-related lifestyle instrument.

The fruit-related lifestyle instrument can be used in studies focused on fruit consumer segmentation. Results provide insight into fruit marketing and distribution companies, which can adjust their marketing strategies and customer-oriented initiatives tailored for specific consumer segments. Results can be useful also for policymakers to promote increased fruit consumption.

Among the growing interest towards market segmentation and targeted marketing, the current study adopted a scientometric approach to examine the literature on wine selection and preferences. The current review specifically attempts to shed light on the research that explores the determinants of wine preferences at multiple levels of analysis.

CiteSpace was used to compute a Document Co-Citation Analysis (DCA) on a sample of 114,048 eligible references obtained from 2,846 publications downloaded from Scopus on 24 May 2021.

An optimized network of 1,505 nodes and 4,616 links was generated. Within the network, impactful publications on the topic and thematic domains of research were identified. Specifically, two thematic macro-areas were identified through a qualitative analysis of papers included in the 7 major clusters. The first one - “Methods of Wine Making” - included clusters #0, #3, #5, #6 and #18. The second one - “Consumers' Attitudes and Preferences Towards Wine” - included clusters #1 and #2. The first thematic macro-area included more technical aspects referring to the process of wine making, while the second thematic macro-area focused more on the factors influencing individuals' preferences and attitudes towards wine. To reflect the aims of the current paper, publications giving light to the “Consumers' Attitudes and Preferences Towards Wine” macro-area were analyzed in detail.

The resulting insights may help wine makers and wine sellers optimize their work in relation to market segments and to the factors influencing individuals' purchasing behaviors.

The study aims to demonstrate that farmers' markets can represent a model of environmental, social and governance reference for modern agri-food systems facing the challenge of post COVID-19 pandemic reconstruction, responding to consumer expectations in terms of health, safety and wholesomeness of agri-food products.

A sample of consumers was surveyed in farmers' markets and social network analysis (SNA) was adopted as a methodological approach to reconstruct the links between the worlds of production and consumption and to derive the relative importance attributed to various factors that promote relational structures.

The work demonstrates the importance of sustainability – as a productive and behavioural model of firms – for the construction of efficient and durable relationship systems in two farmer markets in Sicily. In particular, four fundamental components emerge in the construction of networks represented by consumer sensitivity to sustainability processes, the individual behavioural model of purchasing and consumption, the expectation of political direction and the level and factors of knowledge of the firm. The clustering elements of the relationships were found to be the territory and local products, the environmentalist attitude and the protection of resources, as well as the adoption of a rational waste disposal policy, the fight against food waste, the encouragement of healthier and more sustainable consumption styles, clear and transparent communication and the activation of sustainable supply chain processes in line with the Sustainable Development Goals (SDGs).

The paper aims to demonstrate how alternative food systems can become a useful model for large enterprises, which are committed to rebuilding their business strategy to overcome the current crisis.

This exploratory study examines and comprehends the relative importance of mobile app attributes from a consumer perspective. Both quantitative and qualitative analysis approaches explore users' behavior and attitudes toward the priorities of mobile app attributes and preferences, identifying correlations between attributes and aggregating individual attributes into groups.

Online convenience sampling and snowball sampling resulted in 417 valid responses. The numerical data are analyzed using the relative to an identified distribution (RIDIT) scoring system and gray relational analysis (GRA), and qualitative responses are investigated using text-mining techniques.

This study finds enhanced nuances of user preferences and provides data-driven insights that might help app developers and marketers create a distinct app that will add value to consumers. The latent semantic analysis indicates relationship structure among the attributes, and text-based cluster analysis determines the subsets of attributes that represent the unique functions of the mobile app.

This study reveals the essential components of mobile apps, paying particular attention to the consumer value component, which boosts user approval and encourages prolonged use. Overall, the results demonstrate that developers must concentrate on its functional, technical and esthetic features to make an app more exciting and practical for potential users.

Most scholarly research on apps has focused on their technological merits, aesthetics and usability from the user's perspective. A post-adoption multi-attribute app analysis using both structured and unstructured data is conducted in this study.

The coffee industry has experienced two major trends: the development of connoisseur consumption of specialty coffee and the importance of sustainability. Despite the increasing concomitant relevance of both trends, literature on how sustainability has been interlacing with connoisseur consumption is rather limited. Therefore, this paper aims to analyse how connoisseur consumers (CC) integrate sustainability into their coffee consumption practices.

The paper adopts a qualitative netnographic approach through an interpretive cultural analysis of specialty coffee bloggers narratives, conceived as a specific sub-group of CC that tend to be particularly active on social media.

Through the lens of social practice theories, the study reveals that CC are likely to implement and perceive sustainability very differently from the dominant mass market as subject to the influence of their shared rituals, values, norms and symbolic meanings. Such findings are relevant under a managerial perspective as they also generate insights on how to foster environmentally friendly practices in coffee consumers as well as on how to create more sustainable marketing strategies.

The study contributes to the literature on coffee consumption behaviour and sustainability. First, by analysing actual behaviours rather than intended, the study offers an alternative approach to the dominant paradigm of linear decisions models in the study of sustainable consumption. Second, because CC possess a unique consumption style, different from the mainstream market, the analysis has led towards the identification of alternative sustainable consumption patterns and enablers.

An individual’s driving style significantly affects overall traffic safety. However, driving style is difficult to identify due to temporal and spatial differences and scene heterogeneity of driving behavior data. As such, the study of real-time driving-style identification methods is of great significance for formulating personalized driving strategies, improving traffic safety and reducing fuel consumption. This study aims to establish a driving style recognition framework based on longitudinal driving operation conditions (DOCs) using a machine learning model and natural driving data collected by a vehicle equipped with an advanced driving assistance system (ADAS).

Specifically, a driving style recognition framework based on longitudinal DOCs was established. To train the model, a real-world driving experiment was conducted. First, the driving styles of 44 drivers were preliminarily identified through natural driving data and video data; drivers were categorized through a subjective evaluation as conservative, moderate or aggressive. Then, based on the ADAS driving data, a criterion for extracting longitudinal DOCs was developed. Third, taking the ADAS data from 47 Kms of the two test expressways as the research object, six DOCs were calibrated and the characteristic data sets of the different DOCs were extracted and constructed. Finally, four machine learning classification (MLC) models were used to classify and predict driving style based on the natural driving data.

The results showed that six longitudinal DOCs were calibrated according to the proposed calibration criterion. Cautious drivers undertook the largest proportion of the free cruise condition (FCC), while aggressive drivers primarily undertook the FCC, following steady condition and relative approximation condition. Compared with cautious and moderate drivers, aggressive drivers adopted a smaller time headway (THW) and distance headway (DHW). THW, time-to-collision (TTC) and DHW showed highly significant differences in driving style identification, while longitudinal acceleration (LA) showed no significant difference in driving style identification. Speed and TTC showed no significant difference between moderate and aggressive drivers. In consideration of the cross-validation results and model prediction results, the overall hierarchical prediction performance ranking of the four studied machine learning models under the current sample data set was extreme gradient boosting > multi-layer perceptron > logistic regression > support vector machine.

The contribution of this research is to propose a criterion and solution for using longitudinal driving behavior data to label longitudinal DOCs and rapidly identify driving styles based on those DOCs and MLC models. This study provides a reference for real-time online driving style identification in vehicles equipped with onboard data acquisition equipment, such as ADAS.