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A meta-analytic examination of the antecedents explaining the intention to use fintech

A meta-analytic examination of the antecedents explaining the intention to use fintech This 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Management & Data Systems Emerald Publishing

A meta-analytic examination of the antecedents explaining the intention to use fintech

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References (90)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0263-5577
DOI
10.1108/imds-03-2022-0170
Publisher site
See Article on Publisher Site

Abstract

This 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.

Journal

Industrial Management & Data SystemsEmerald Publishing

Published: Mar 9, 2023

Keywords: Fintech; UTAUT2; Intention to use; Meta-analysis; Relative weight analysis

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