对解释使用金融科技意图的前因的元分析检验

W. Bommer, Emil Milevoj, Shailesh Rana
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引用次数: 5

摘要

本研究考察了金融科技使用意愿的前因,以确定哪些前因可以提供简洁但准确的解释。设计/方法/方法基于42个样本的荟萃分析估计了7个前因如何与金融科技使用意图相关联。随后的分析利用元分析来估计回归分析,同时估计前事和金融科技使用意愿之间的关系。然后进行相对权重分析,确定每个先行项的效用。发现理性动机、价格价值、业绩预期和社会影响力与金融科技使用意愿的关系最为密切。进一步的分析发现,一个只有三个前因式的简约模型的预测效果几乎与完整的七个前因式模型一样。四个调节变量被检查,但发挥次要作用。研究局限性/意义由于所有的初步研究都采用了横断面调查,因此常用方法的差异可能会影响研究结果。实际意义很少有措施(即三个)可以强有力地解释金融科技使用意图。当这些措施不能轻易受到影响时,还会提出替代办法。原创性/价值这是对金融科技使用意图的首次综合评估。作者整合了目前已知的金融科技使用意图,然后提供了一个研究人员和从业人员都可以利用的金融科技使用意图的稳健模型。
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A meta-analytic examination of the antecedents explaining the intention to use fintech
PurposeThis study examines antecedents to fintech use intention to determine which antecedents can provide a parsimonious, yet accurate explanation.Design/methodology/approachMeta-analyses based on 42 samples estimate how seven antecedents are associated with fintech use intentions. Subsequent analyses utilize meta-analyses to estimate a regression analysis to simultaneously estimate the relationship between the antecedents and fintech use intention. Relative weight analysis then determined each antecedent's utility.FindingsHedonic motivation, price value, performance expectations and social influence had the strongest relationships with intention to use fintech. Further analyses found a parsimonious model with only three antecedents was nearly as predictive as the full seven antecedent model. Four moderating variables were examined but played minor roles.Research limitations/implicationsCommon method variance may impact the findings because all primary studies used cross-sectional surveys.Practical implicationsVery few measures (i.e. three) can robustly explain fintech use intention. When these measures cannot be readily influenced, alternatives are also presented.Originality/valueThis is the first integrative review of fintech use intentions. The authors integrate what is currently known about fintech use intentions and then provide a robust model for fintech use intentions that both researchers and practitioners can utilize.
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