金融科技应用中的性别差异：我们知道什么，还需要知道什么？

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2023-12-26 DOI:10.1108/jm2-06-2023-0121

Vinki Rani, Jitender Kumar

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引用次数: 0

摘要

目的本研究旨在确定哈里亚纳邦（印度）采用金融技术（FinTech）的决定因素。此外，作者还比较了男性和女性受访者的行为意向，以全面了解金融科技的采用情况。设计/方法/途径本研究包含两项关于男性和女性的横截面调查。研究 M 是针对（333 名）男性用户完成的，研究 F 是针对（317 名）女性用户进行的。研究结果研究结果表明，在研究 M 和研究 F 中，感知有用性和感知易用性对态度和行为意向有很大影响。此外，结果表明，感知价值对行为意向的影响很大，而感知风险对行为意向的影响很小。令人惊讶的是，相对优势（在研究 M 中）和可试用性（在研究 F 中）对行为意向的影响并不明显。此外，研究结果还证实，在这两项研究（M 和 F）中，态度和行为意向对金融科技的实际使用产生了重大影响。原创性/价值据作者所知，这是关于金融科技的初步研究，探讨了性别在科技采用过程中的作用。男性和女性在采用方面的差异以及作者发现的具有洞察力的结果有助于揭示背景的独特性。本研究也是初步检验三个可靠的技术决定因素理论的研究之一，然后为金融科技的实际使用提供了一个稳健的模型，可供从业人员和研究人员使用。

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Gender differences in FinTech adoption: What do we know, and what do we need to know?

Purpose

This study aims to identify the determinants of adopting financial technology (FinTech) in Haryana (India). Further, the authors also compare the behavioural intention among male and female respondents to deliver a comprehensive understanding of the adoption of FinTech.

Design/methodology/approach

The current study contains two cross-sectional surveys about males and females. Study M is completed with (333) males, and Study F is conducted on (317) female users towards FinTech adoption. This study used “Partial least squares-structural equation modelling (PLS-SEM)” for data analysis.

Findings

The outcomes indicate that in both (Studies M and F), perceived usefulness and perceived ease of use substantially impact attitude and behavioural intention. Moreover, the results show that perceived value significantly influences, while perceived risks insignificantly influence behavioural intention. Surprisingly, relative advantage (in Study M) and trialability (in Study F) has insignificant impact on behavioural intention. Further, the outcomes also confirm that in both studies (M and F), attitude and behavioural intention substantially influence the actual use of FinTech.

Originality/value

To the best of the authors’ knowledge, this is the preliminary research on FinTech to inspect the role of gender in the technology adoption process. The adoption difference between males and females and the insightful result that the authors found help shed light on the uniqueness of the context. This study is also one of the initial to test three credible technology determinant theories and then offer a robust model for the actual use of FinTech that is to be used by both practitioners and researchers.

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.