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.

This study highlights the influence of diverse kinds of persuasive messages on intention to stay home during COVID-19 pandemic. The COVID-19 pandemic is believed to be the top modern societal challenge necessitating extensive collective action and collaboration. The statistical population included Iranian individuals by answering our online survey questionnaires at diverse phases of the COVID-19 pandemic. 406 completed questionnaires were gathered and analyzed. Note that, according to the official reports, Iran encountered COVID-19 disease since February 19, 2020. Data collection was started on July 18, 2020 (at the beginning of second wave of COVID-19 in Iran) and lasted until October 30, 2020. Various social network platforms including Instagram, WhatsApp, Facebook and Telegram were used for distribution of the questionnaires. According to the results, perceived severity and perceived self-efficacy had an insignificant direct influence on intention to stay home. From another perspective, perceived vulnerability (beta = 0.261, CI = 0.059; 0.242]) and perceived response efficacy (beta = 0.502, CI = 0.347; 0.656]) positively and significantly affected the intention to stay home. Moreover, no significant difference was found between these two kinds of media messages in research sample. Our findings showed that "perceived response efficacy " has the highest importance score of 0.502; if the perceived response efficacy performance is boosted by one unit point by individuals during COVID-19 pandemic, its overall intention to stay home will increase by 0.502. Besides, our results showed that lowest performance (78.104) is associated with perceived vulnerability highlighting an excellent opportunity for improvement in this area.