The effect of social media marketing on voting intention; an application of multidimensional panel data

PurposeThis study examines the effect of social media marketing on voting intention applying a combination of fuzzy logic methodology and a multidimensional panel data model.Design/methodology/approachThe study adopts a multidimensional panel data method that includes several fixed effects. The dependent variable is a multifaceted construct that measures the participants' intention to vote. The independent variables are electronic word of mouth (eWOM), customisation (CUS), entertainment (ENT), interaction (INT), trendiness (TRD), candidate's perceived image (CPI), religious beliefs (RB), gender and age. The grouping variables that signify fixed effects are employment status, level of education, mostly used social media and religion. First, the significance of said fixed effects was tested through an ANOVA process. Then, the main model was estimated, including the significant grouping variables as fixed effects.FindingsEmployment status and level of education were significant fixed effects. Also, eWOM, ENT, INT, CPI, RB and gender significantly affected participants' voting intention.Research limitations/implicationsBeing based on a questionnaire that asked participants about how they perceive different aspects of social media, the present study is limited to their perceptions. Therefore, further studies covering the voters' behaviour in action could be efficient complements to the present study.Practical implicationsThe findings could guide the political parties into prioritizing the aspects of social media in forming an effective campaign resulting in being elected.Social implicationsThe findings have the potential to help the public in making better informed decisions when voting. Furthermore, the results of this study indicate applications for social media which are beyond leisure time fillers.Originality/valueFuzzy logic and multidimensional panel data estimates are this study's novelty and originality. Structural equation modelling and crisp linguistic values have been used in previous studies on social media's effect on voting intent. The former refines the data gathered from a questionnaire, and the latter considers the possibility of including different grouping factors to achieve a more efficient and less biased estimation.