Segmenting female students' perceptions about Fintech using Explainable AI.

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2024-12-12 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1504963
Christos Adam
{"title":"Segmenting female students' perceptions about Fintech using Explainable AI.","authors":"Christos Adam","doi":"10.3389/frai.2024.1504963","DOIUrl":null,"url":null,"abstract":"<p><p>The use of Financial Technology (Fintech) has been proposed as a promising way to bridge the gender gap, both financially and socially. However, there is evidence that Fintech is far from achieving this objective, and that women's perceptions of Fintech usages are not clear. Therefore, the main objective of the this study is to segment women's perceptions toward Fintech tools and interpret these segments using machine learning methods. Two primary segments of women were produced, namely a \"Fintech-friendly\" group and a \"Fintech-sceptical\" group. The importance and reasonings behind the aforementioned segmentation are then examined. The most prominent factors affecting a woman being in the \"Fintech-friendly\" group are the perceived benefits of Fintech tools compared to the traditional ones, such as ease of usage, time-space convenience, and its advantageous nature. Finally, for Fintech stakeholders, implications for usability, ease, Fintech education, and tailored experiences may be advantageous approaches.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1504963"},"PeriodicalIF":3.0000,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11670257/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frai.2024.1504963","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

The use of Financial Technology (Fintech) has been proposed as a promising way to bridge the gender gap, both financially and socially. However, there is evidence that Fintech is far from achieving this objective, and that women's perceptions of Fintech usages are not clear. Therefore, the main objective of the this study is to segment women's perceptions toward Fintech tools and interpret these segments using machine learning methods. Two primary segments of women were produced, namely a "Fintech-friendly" group and a "Fintech-sceptical" group. The importance and reasonings behind the aforementioned segmentation are then examined. The most prominent factors affecting a woman being in the "Fintech-friendly" group are the perceived benefits of Fintech tools compared to the traditional ones, such as ease of usage, time-space convenience, and its advantageous nature. Finally, for Fintech stakeholders, implications for usability, ease, Fintech education, and tailored experiences may be advantageous approaches.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用可解释人工智能细分女学生对金融科技的看法。
金融科技(Fintech)的使用被认为是弥合经济和社会性别差距的一种有希望的方式。然而,有证据表明,金融科技远未实现这一目标,女性对金融科技用途的看法也不清楚。因此,本研究的主要目的是细分女性对金融科技工具的看法,并使用机器学习方法解释这些细分。研究产生了两个主要的女性群体,即“金融科技友好”群体和“金融科技怀疑”群体。然后检查上述分割背后的重要性和原因。影响女性加入“金融科技友好型”群体的最重要因素是,与传统工具相比,金融科技工具的感知优势,如易用性、时空便捷性及其优势性质。最后,对于金融科技利益相关者来说,可用性、易用性、金融科技教育和量身定制的体验可能是有利的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
期刊最新文献
Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: a systematic literature review. The technology acceptance model and adopter type analysis in the context of artificial intelligence. An analysis of artificial intelligence automation in digital music streaming platforms for improving consumer subscription responses: a review. Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm. SineKAN: Kolmogorov-Arnold Networks using sinusoidal activation functions.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1