Does Gender Matter for Electronic Word-of-Mouth Interactions in Social Media Marketing Strategies? An Empirical Multi-Sample Approach

Considering social media’s expansion worldwide, marketing academics and marketers emphasize the need to consider electronic word of mouth (eWOM) for strategic marketing decisions. However, there is limited research regarding the ways in which male and female consumers engage in eWOM behaviors. This research aims to explore the gender-specific dynamics of eWOM drivers in social media marketing, by validating a proposed model of key predictors for two samples (of female and male respondents). Data were gathered from two samples of social media users. For this empirical research, we integrated structural equation modeling and an artificial neural network (PLS-SEM-ANN) for a comprehensive approach intended to generate practical and theoretical insights for eWOM. Hypothesis testing reflected contrasting results—for female respondents, the key eWOM drivers were customer participation, involvement, loyalty, and customer satisfaction; whereas, for male respondents, the key predictors were brand familiarity, loyalty, and satisfaction. The significant variables supported by SEM were included in ANN models as input neurons, showcasing nonlinear relationships among constructs for both samples. Thus, this research provides theoretical contributions regarding eWOM, gender assessments, and the social media marketing literature. From a practical perspective, this study advances targeted social media marketing strategies to enhance consumer–brand interactions.