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The emergence of social media platforms as the main representatives of Web 3.0 applications significantly impacts the co-creation activities among enterprises, customers, and other stakeholders, and has enabled firms to benefit from creativity and ideas of their users and customers for developing and rendering innovative services. This study aims to investigate how the co-creation activities of users on social media platforms have an effect on the enterprises’ innovative services. For this purpose, the authors surveyed customers of innovative services who used social media platforms to meet their needs from the enterprises that innovatively offer such services. An online questionnaire was designed and distributed among the sample of customers, and 505 completed questionnaires were analyzed following the PLS-SEM approach. The findings revealed that customer citizenship behavior and customer participation behavior on social media platforms positively affect the rendering of innovative services. Findings also highlighted that an increase in social co-creation activities, as moderator, positively affects customer citizenship behavior on service innovativeness, and negatively affects customer participation behavior on service innovativeness. The findings of this research could be useful for entrepreneurs and managers of the enterprises that offer innovative services to efficiently use social media tools to benefit from the customers’ co-creation activities and to perform more competitively and sustainably in a hostile business environment.

The purpose of this paper is to reveal how social network marketing (SNM) can affect consumers’ purchase behavior (CPB). We used the combination of structural equation modeling (SEM) and unsupervised machine learning approaches as an innovative method. The statistical population of the study concluded users who live in Hungary and use Facebook Marketplace. This research uses the convenience sampling approach to overcome bias. Out of 475 surveys distributed, a total of 466 respondents successfully filled out the entire survey with a response rate of 98.1%. The results showed that all dimensions of social network marketing, such as entertainment, customization, interaction, WoM and trend, had positively and significantly influenced consumer purchase behavior (CPB) in Facebook Marketplace. Furthermore, we used hierarchical clustering and K-means unsupervised algorithms to cluster consumers. The results show that respondents of this research can be clustered in nine different groups based on behavior regarding demographic attributes. It means that distinctive strategies can be used for different clusters. Meanwhile, marketing managers can provide different options, products and services for each group. This study is of high importance in that it has adopted and used plspm and Matrixpls packages in R to show the model predictive power. Meanwhile, we used unsupervised machine learning algorithms to cluster consumer behaviors.