The sharing economy in a digital society: youth consumer behavior in Italy

Gazzola, Patrizia; Grechi, Daniele; Papagiannis, Fragkoulis; Marrapodi, Chiara

doi:10.1108/k-12-2019-0796

Cited by 12 publications

(6 citation statements)

References 51 publications

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“…However, our data reveal that slightly more attention is paid to healthy and second-hand products. This is consistent with observations of current sustainable consumption phenomena, such as those that are related to second-hand and refurbished goods as well as to leasing and sharing services (Copeland and Masa, 2022; Gaur et al , 2022; Gazzola et al , 2021).…”

Section: Discussionsupporting

confidence: 91%

Sustainability advocacy antecedents: how social media content influences sustainable behaviours among Generation Z

Confetto

Covucci

Addeo

et al. 2023

JCM

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Purpose The young members of Generation Z, who are hyperconnected and addicted to social media, are thought to be particularly sensitive to environmental and social concerns. This study aims to draw on a conceptual model that is based on the stimulus-organism-response paradigm. Exposure to sustainability content on social media is considered to be a stimulus that affects the development of sustainability advocacy among GenZers, who modify their lifestyles. Five hypotheses are developed and tested. The goal is to define the antecedents of sustainability advocacy. Design/methodology/approach A Web survey was distributed to 660 Italian members from Generation Z (aged between 14 and 25) to detect the frequencies of exposure to sustainability content on social media, sustainable habits, sustainable consumption behaviours and actions that are related to sustainability advocacy on social media. Correlation and multiple regression analyses were conducted to investigate the relationships between these factors. Findings The results show that exposure to sustainability content on social media affects both sustainable habits and sustainable consumption behaviour. These three factors influence the propensity to promote sustainability-related issues on social media and should, therefore, be considered to be antecedents of sustainability advocacy. Practical implications The study, which takes the social responsibilities of large companies into account, is conducive to understanding how brands can intervene in the soliciting processes of sustainability advocacy through social media to gain legitimacy and increase brand awareness. Originality/value This study is among the first to consider the use of social media for advocating sustainability among Generation Z, thus enriching academic research on this cohort.

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Section: Discussionsupporting

confidence: 91%

Sustainability advocacy antecedents: how social media content influences sustainable behaviours among Generation Z

Confetto

Covucci

Addeo

et al. 2023

JCM

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“…The shared revolution changes the economy of the 21st century, influencing significantly the transformation of the socio-economic system of consumers. We are witnessing a widely understood change in the current value system or approach to ownership, in which the issue of trust in the individual begins to play a special role (Gazzola et al, 2020). The global economy is struggling with a serious transformation, where the sharing economy -"like a virus" seizes markets in individual countries, operating in subsequent segments of life.…”

Section: R I Ementioning

confidence: 99%

“…Research of authors also show the importance of other factors in relation to the behaviour of consumers in sharing. According to Gazzola et al, (2020) both socio-demographic and economic diversity (gender, age, place of residence, wages etc.) affect various spheres of people's lives, determining their decisions and choices.…”

Section: Literature Reviewmentioning

confidence: 99%

Trust and other factors impacting the platform choice in sharing economy: A case from Poland

Rakowska¹

2021

IER

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The purpose of this study is two-fold: firstly, to identify the role of trust in the sharing economy; secondly, to identify the correlations between personal determinants such as age, gender, education level, place of residence and factors impacting the choice of sharing economy platform. Research Design & Methods:The mix methods were applied in the study. Firstly, the literature studies were used to recognise the theoretical background and the variables identification. Secondly, the pre-research study, applying the in-depth interviews, was conducted, and thirdly, the main primary empirical research was employed with the online survey as a main research method. Findings: There is a strong relationship between the activities of consumers in relation to sharing economy and trust. Trust becomes a fundamental issue in the so-called "the trend of sharing". Consumers identify trust as a key determinant of their participation in the sharing economy and likewise the main limitation in case of their deeper involvement in the sharing process. The more credible and trustworthy an enterprise is, the more willingly consumers are able to take advantage of its intermediation. When analysing consumer decisions in relation to independent variables, such as age, gender, place of residence and education level, a lack of statistically significant correlations between the variables and the factors influencing the choice of the appropriate platform was noticed. Only two pairs of variables showed statistically significant negative correlations: age and credibility of the sharing economy platform as well as education level and number of stars. Implications & Recommendations: Research broadens knowledge about the sharing economy and allows to better understand the behaviour of active consumers of the sharing economy. The results may be useful for sharing economy companies and platforms. Contribution & Value Added: The analysis presented in the paper expands knowledge about the significance of trust and other factors influencing the choice of platforms in sharing economy. Article type:research article

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“…This wave of digitization highlights the need for a more efficient and more innovative society. This wave of digitization highlights the fact that digital technologies such as cloud computing and artificial intelligence are not just technological innovations but are also profoundly changing people's lifestyles and comfort levels (Gazzola et al, 2021). Cloud computing provides more flexible and convenient solutions for storing and processing information.…”

Section: Introductionmentioning

confidence: 99%

Integration and Innovation Path Analysis of Enterprise Marketing Data Management Based on Deep Learning

Wang

2024

ICST Transactions on Scalable Information Systems

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INTRODUCTION: To explore the integration and innovation path of enterprise marketing data management based on deep learning to adapt to today's competitive business environment. With the continuous development of information technology, enterprises are faced with a large amount of marketing data, and how to efficiently manage and integrate these data has become an essential issue for enterprises to improve their market competitiveness. Deep learning, as a necessary technical means of artificial intelligence, provides enterprises with more intelligent and precise data processing tools.OBJECTIVES: The primary purpose of the study is to solve the problems of marketing data management in traditional enterprises and to achieve better integration and management of data through deep learning technology. Specifically, the goal is to explore the potential of deep learning in improving data processing efficiency and accurately analyzing user behavior and trends. By achieving these goals, organizations can better understand market needs, develop more effective marketing strategies, and stand out in a competitive marketplace.METHODS: This study adopts a comprehensive approach, including a literature review, case study, and empirical analysis of deep learning algorithms. First, the main issues of current enterprise marketing data management and the latest progress in deep learning were understood through an in-depth study of the literature in related fields. Second, several enterprise cases were selected to gain a deeper understanding of the challenges and needs of enterprises in marketing data management through field research and data collection. Finally, a series of deep learning algorithms were designed and implemented to validate their effectiveness in real-world applications and analyze their impact on data integration and innovation paths.RESULTS: The results of the study show that deep learning has significant advantages in enterprise marketing data management. By using deep learning algorithms, enterprises are able to handle large-scale marketing data more efficiently and achieve intelligent data integration and accurate analysis. This not only improves the efficiency of data processing but also provides enterprises with deeper market insights that help develop more targeted marketing strategies.CONCLUSION: The results of the study are of guiding significance for enterprises to realize data-driven marketing decision-making, which provides strong support for enterprises to maintain their competitive advantages in the highly competitive market. Future research can further explore the application of deep learning in different industries and scenarios, as well as how to optimize deep learning algorithms further to meet the changing needs of enterprises.

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The sharing economy in a digital society: youth consumer behavior in Italy

Cited by 12 publications

References 51 publications

Sustainability advocacy antecedents: how social media content influences sustainable behaviours among Generation Z

Sustainability advocacy antecedents: how social media content influences sustainable behaviours among Generation Z

Trust and other factors impacting the platform choice in sharing economy: A case from Poland

Integration and Innovation Path Analysis of Enterprise Marketing Data Management Based on Deep Learning

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