Do eReferral, eWOM, familiarity and cultural distance predict enrollment intention? An application of an artificial intelligence technique. (12th July 2021) - Oday, Akile
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. Abstract : 是否在线推荐, 在线口碑, 熟悉度, 还有文化距离决定参加意图吗？人工智能科技的应用 摘要: 研究目的: 很少有研究检测过在线口碑（eWOM）, 在线推荐（eReferral）, 熟悉度, 和文化距离对行为结果的影响, 特别是在教育旅游的领域里。本论文基于社交网络理论探索eReferral, eWOM, 熟悉度, 和文化距离对参加意图的作用。 研究设计/方法/途径: 研究样本数据为931位教育旅游的游客, 通过判断抽样技术。本论文通过线性建模和人工智能（即人工神经网络）技术来培训和检测提出的关系。 研究结果: 研究结果表明, eRefferal, eWOM, 熟悉度, 和文化距离对参与意图起到决定作用, 其中包括对称（线性建模）和非对称地（人工神经网络）方式。非对称建模将增加有效性和相关性的决定度。 研究原创性/价值: 本论文通过证实提出的关系和采用现代方法, 比如人工神经网络, 对管理文献做出理论和实践的贡献。本论文还讨论了对实践和理论的启示。