Estimating the Impact of User Personality Traits on Word-of-Mouth : Text-Mining Microblogging Platforms

Word-of-mouth (WOM) plays an increasingly important role in shaping consumers’ behavior and preferences as users’ opinions and choices are frequently shared in social media. In this paper, we examine whether latent personality traits of online users accentuate or attenuate the effectiveness of WOM in microblogging platforms. To answer this question, we leverage data-mining and machine-learning methods in combination with econometric techniques utilizing a novel quasi-experiment. Our analysis yields two main results. First, there is a positive and statistically significant effect of the level of personality similarity between two social media users on the likelihood of a subsequent purchase from a recipient of a WOM message after exposure to the WOM message of the sender. In particular, exposure to WOM messages from similar users in terms of personality, rather than dissimilar users, increases the likelihood of a post-purchase by 47.58%. Second, there are statistically significant effects of specific pairwise combinations of personality characteristics of senders and recipients of WOM messages on the effectiveness of WOM. For instance, introvert users are responsive to WOM, in contrast to extrovert users. Besides, agreeable, conscientious, and open social media users are more effective disseminators of WOM. In addition, WOM originating from users with low levels of emotional range affects similar users whereas for high levels of emotional range increased similarity has usually the opposite effect. The examined effects are also of significant economic importance as, for instance, a WOM message from an extrovert user to an introvert peer increases the likelihood of a subsequent purchase by 71.28%. Our findings are robust to several alternative methods and specifications, such as controlling for latent user homophily and network structure roles based on deep-learning models. By extending the characteristics that have been theorized to affect the effectiveness of WOM from the observable to the latent space, tapping into users’ latent personality characteristics, and illustrating how companies can leverage the abundance of unstructured data in social media, our paper provides actionable insights regarding the future potential of social media advertising and advanced micro-targeting based on big data and deep learning