Do eReferral, eWOM, familiarity and cultural distance predict enrollment intention? An application of an artificial intelligence technique

Abstract

Purpose – Little empirical attention has been paid to the effects of electronic word-of-mouth (eWOM), electronic referral (eReferral), familiarity and cultural distance on behavioral outcomes, especially within the context of educational tourism. Based on the social network theory, this paper aims to explore the effects of eReferral, eWOM, familiarity and cultural distance on enrollment intention. Design/methodology/approach – Survey data (n = 931) were obtained from educational tourists using a judgmental sampling technique. Linear modeling and artificial intelligence (i.e. artificial neural network [ANN]) techniques were used for training and testing the proposed associations. Findings – The results suggest that eReferral, eWOM, familiarity and cultural distance predict intention to enroll both symmetrically (linear modeling) and asymmetrically (ANN). The asymmetric modeling possesses greater predictive validity and relevance. Originality/value – This study contributes theoretically and methodologically to the management literature by validating the proposed relationships and deploying contemporary methods such as the ANN. Implications for practice and theory are discussed.